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Geosciences, Volume 14, Issue 8 (August 2024) – 30 articles

Cover Story (view full-size image): Virtual 3-D reconstruction of Marseille basin (Bouches-du-Rhône, France) 1 Myr ago, with its landscape diversity, its edible plants including proto-cereals, fruits and herbaceous plants, and its water resources. Based in particular on pollen records (four examples shown), it was a favorable site for the early Pleistocene hominin migration along the northern shore of the Mediterranean. The Marseilles basin is the third site after Acıgöl and Kocabaş, in south-west Anatolia, to show the presence of proto-cereal pollen well before the start of the Neolithic period 12,000 years ago. View this paper
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27 pages, 8502 KiB  
Article
Spatiotemporal Variability Analysis of Rainfall and Water Quality: Insights from Trend Analysis and Wavelet Coherence Approach
by Syeda Zehan Farzana, Dev Raj Paudyal, Sreeni Chadalavada and Md Jahangir Alam
Geosciences 2024, 14(8), 225; https://doi.org/10.3390/geosciences14080225 - 21 Aug 2024
Viewed by 397
Abstract
An understanding of the trend and relationship between rainfall patterns and water quality dynamics can provide valuable guidelines for the effective management of water resources. The aim of this study was to reveal the synchronous trends in rainfall and water quality and to [...] Read more.
An understanding of the trend and relationship between rainfall patterns and water quality dynamics can provide valuable guidelines for the effective management of water resources. The aim of this study was to reveal the synchronous trends in rainfall and water quality and to explore the potential connection between seasonal variation in rainfall volume and the water quality index. This study scrutinised the seasonal temporal trends of rainfall and water quality parameters of three water supply reservoirs in the Toowoomba region of Australia by applying the modified Mann–Kendall (MMK) test and innovative trend analysis (ITA) methods from data collected over 22 years (2002–2022). The models showed a significant increasing trend of rainfall in two rainfall stations during autumn season. The water quality parameters, such as PO43−, exhibited a significant decreasing trend in all seasons in three reservoirs. On the other hand, the water quality index (WQI) showed a decreasing trend in the Cooby and Cressbrook reservoirs, excepting the Perseverance reservoir, which exhibited an increasing trend. In addition to the detection of trends, this study investigated the potential correlation between seasonal variation of rainfall volume and the water quality index using the wavelet transform coherence (WTC) method. The data of twelve rainfall stations were brought into this analysis. The WTC analysis displayed an apparent correlation between the water quality index and rainfall pattern for 70% of the rainfall stations across 8–16 periods. The highest coherency was noticed in 8–16 periods from 2002–2022, as observed at both the Cooby Creek rainfall station and in the WQI of the Cooby reservoir. This evaluation revealed the intertwined dynamics of rainfall patterns and water quality, providing a deeper understanding of their interdependence and implications, which might be useful for environmental and hydrological management practices. Full article
(This article belongs to the Section Climate)
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Figure 1

Figure 1
<p>Study area map. The left map shows the area of rainfall stations, including the location of Cooby, Cressbrook and Perseverance reservoirs. The right map is the map of Australia, in which the blue highlighted area is the state of Queensland and the red marks indicate the approximate location of reservoirs.</p>
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<p>The schematic framework of the research method.</p>
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<p>Spatial distribution of rainfall: (<b>a</b>) mean (RF), (<b>b</b>) coefficient of variation (CV) (%), and (<b>c</b>) precipitation concentration index (PCI).</p>
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<p>Spatial distribution of autumn rainfall trend parameters. Map (<b>a</b>) Z statistics, map (<b>b</b>) Sen’s Slope, and map (<b>c</b>) B (ITA).</p>
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<p>Spatial distribution of Winter rainfall trend parameters. Map (<b>a</b>) is Z statistics, Map (<b>b</b>) is Sen’s Slope, and Map (<b>c</b>) is B (ITA).</p>
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<p>Spatial distribution of spring rainfall trend parameters. Map (<b>a</b>) Z statistics, map (<b>b</b>) Sen’s slope, and map (<b>c</b>) B (ITA).</p>
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<p>Spatial distribution of summer rainfall trend parameters. Map (<b>a</b>) Z statistics, map (<b>b</b>) Sen’s slope, and map (<b>c</b>) B (ITA).</p>
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<p>Plot of the innovative trend of WQI time series of Cooby reservoir (2001–2022). The blue dots represent the WQI in different seasons.</p>
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<p>Plot of the innovative trend of the WQI time series of Cressbrook reservoir (2001–2022). The blue dots represent the WQI in different seasons.</p>
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<p>Plot of the innovative trend of WQI time series of Perseverance reservoir (2001–2022). The blue dots represent the WQI in different seasons.</p>
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<p>Modified Mann–Kendall test Z statistic plot, (<b>top</b>) Cooby, (<b>middle</b>) Cressbrook, (<b>bottom</b>) Perseverance.</p>
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<p>WTC plot of Rainfall and WQI (Cooby). The coherency is shown by colour code, ranging from red (high coherency, value close to 1) to blue (low coherency, value close to 0). Statistically significant periodicity is displayed with black contours indicating a 5% significance level. The time scale ‘Year’ is shown in x axis and vertical axis (y axis) presents ‘Period’ across seasons in a year. The twelve rainfall stations are marked with lettering ranging (<b>a</b>–<b>l</b>). Individual plot of each station is available in the link of <a href="#app1-geosciences-14-00225" class="html-app">Supplementary Files</a>.</p>
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<p>WTC plot of Rainfall and WQI (Cressbrook). The coherency is shown by colour code ranging from red (high coherency, value close to 1) to blue (low coherency, value close to 0). Statistically significant periodicity is displayed with black contours indicating a 5% significance level. The time scale ‘Year’ is shown in x axis and vertical axis (y axis) presents ‘Period’ across seasons in a year. The twelve rainfall stations are marked with lettering ranging from (<b>a</b>–<b>l</b>). Individual plot of each station is available in the link of <a href="#app1-geosciences-14-00225" class="html-app">Supplementary Files</a>.</p>
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<p>WTC plot of Rainfall and WQI (Perseverance). The coherency is shown by colour code ranging from red (high coherency, value close to 1) to blue (low coherency, value close to 0). Statistically significant periodicity is displayed with black contours indicating a 5% significance level. The time scale ‘Year’ is shown in x axis and vertical axis (y axis) presents ‘Period’ across seasons in a year. The twelve rainfall stations are marked with lettering ranging from (<b>a</b>–<b>l</b>). Individual plot of each station is available in the link of <a href="#app1-geosciences-14-00225" class="html-app">Supplementary Files</a>.</p>
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23 pages, 11373 KiB  
Article
The Origins of the Hydrogen Sulphide (H2S) Gas in the Triassic Montney Formation, British Columbia, Canada
by Gareth Chalmers, Pablo Lacerda Silva, Amanda Bustin, Andrea Sanlorenzo and Marc Bustin
Geosciences 2024, 14(8), 224; https://doi.org/10.3390/geosciences14080224 - 21 Aug 2024
Viewed by 405
Abstract
The inexplicable distribution of souring wells (presence of H2S gas) of the unconventional Montney Formation hydrocarbon resource (British Columbia; BC) is investigated by analysing sulphur and oxygen isotopes, coupled with XRD mineralogy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX). [...] Read more.
The inexplicable distribution of souring wells (presence of H2S gas) of the unconventional Montney Formation hydrocarbon resource (British Columbia; BC) is investigated by analysing sulphur and oxygen isotopes, coupled with XRD mineralogy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDX). The sulphur isotopic analysis indicates that the sulphur isotopic range for Triassic anhydrite (δ34S 8.9 to 20.98‰ VCDT) is the same as the H2S sulphur that is produced from the Montney Formation (δ34S 9.3 to 20.9‰ VCDT). The anhydrite in the Triassic rocks is the likely source of the sulphur in the H2S produced in the Montney Formation. The deeper Devonian sources are enriched in 34S and are not the likely source for sulphur (δ34S 17.1 and 34‰ VCDT). This is contradictory to studies on Montney Formation producers in Alberta, with heavier (34S-enriched) sulphur isotopic signatures in H2S gas of all souring Montney Formation producers. These studies conclude that deep-seated faults and fractures have provided conduits for sulphate and/or H2S gas to migrate from deeper sulphur sources in the Devonian strata. There are several wells that show a slightly heavier (34S-enriched) isotopic signature (δ34S 18 to 20‰ VCDT) within the Montney Formation H2S gas producing within close proximity to the deformation front. This variation may be due to such deep-seated faults that acted as a conduit for Devonian sulphur to migrate into the Montney Formation. Our geological model suggests the sulphate-rich fluids have migrated from the Charlie Lake Formation prior to hydrocarbon generation in the Montney Formation (BC). Sulphate has concentrated in discrete zones due to precipitation in conduits like fracture and fault systems. The model fits the observation of multi-well pads containing both sour- and sweet-producing wells indicating that the souring is occurring in very narrow and discrete zones with the Montney Formation (BC). Government agencies and operators in British Columbia should map the anhydrite-rich portions of the Charlie Lake Formation, together with the structural elements from three-dimensional seismic to reduce the risk of encountering unexpected souring. Full article
(This article belongs to the Section Geochemistry)
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Figure 1

Figure 1
<p>Location map for the four wells (blue stars) that were analysed to determine the sources of sulphur and the origins of the hydrogen sulphide in British Columbia. In addition, the sulphur isotopic analyses from other studies have been included in this report with a total of 218 mineral and organic matter samples analysed (see <a href="#geosciences-14-00224-t0A1" class="html-table">Table A1</a>). The Well Authorization number for the four wells are also shown on the map. The green squares represent towns, the small black lines are horizontal wells, and small black circles are vertical wells that are Montney (sweet) producers in British Columbia. The red circles represent Montney sour producers. The pink box is the location of the well pads shown in <a href="#geosciences-14-00224-f002" class="html-fig">Figure 2</a> and the green star is the location of stratigraphy and log response shown in <a href="#geosciences-14-00224-f004" class="html-fig">Figure 4</a>.</p>
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<p>An example of the inexplicable distribution of sweet- (no H<sub>2</sub>S present) and sour- (H<sub>2</sub>S present) producing wells in the Triassic Montney Formation in British Columbia. Sour wells occur in all three informal Montney members (lower, middle and upper) and a sour well can be producing only hundreds of metres away from a sweet well. Our model incorporates this observation seen within the producing fields of British Columbia. The location of this group of wells is shown in <a href="#geosciences-14-00224-f001" class="html-fig">Figure 1</a> as a pink box. The log signatures of the upper, middle, and lower Montney can be seen in <a href="#geosciences-14-00224-f004" class="html-fig">Figure 4</a>.</p>
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<p>The Triassic stratigraphy of British Columbia that includes the Halfway, Doig, and Montney formations, which unconformably overlie the Permian Belloy Formation. The Montney Formation corresponds to the Lower Triassic section and is part of the Daiber Group, along with the overlying Doig Formation (modified after [<a href="#B41-geosciences-14-00224" class="html-bibr">41</a>,<a href="#B42-geosciences-14-00224" class="html-bibr">42</a>,<a href="#B43-geosciences-14-00224" class="html-bibr">43</a>,<a href="#B44-geosciences-14-00224" class="html-bibr">44</a>]; eustatic levels after [<a href="#B45-geosciences-14-00224" class="html-bibr">45</a>]).</p>
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<p>Stratigraphy and log response (gamma, bulk density, and resistivity) of the Halfway, Doig, Montney, and Belloy formations in well 200/C-078-C-094-H-05/00 (see <a href="#geosciences-14-00224-f001" class="html-fig">Figure 1</a> for location). The Montney Formation is informally subdivided into the upper, middle, and lower Montney formations based on the sequence stratigraphic model of [<a href="#B46-geosciences-14-00224" class="html-bibr">46</a>]. Abbreviation: TVD, total vertical depth.</p>
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<p>Cross-plot of sulphur and oxygen isotopes from anhydrite, pyrite, kerogen, and H<sub>2</sub>S gas from both Triassic and Devonian sources. Point data are the oxygen and sulphur isotopic data for anhydrite minerals for both Triassic and Devonian samples. The Triassic rocks include the Charlie Lake, Halfway, Doig, Montney, and Belloy formations. Triassic-sourced sulphur from anhydrite minerals all plot within the green box and are <sup>34</sup>S-depleted compared to the Devonian-sourced sulphur (<sup>34</sup>S-enriched) from anhydrite (blue box). Samples from the Devonian Muskeg Formation in this study (purple triangles) have the isotopically lightest (<sup>34</sup>S-depleted) Devonian sulphur/oxygen anhydrite, and with the two samples from the Doig (grey diamonds) and Halfway (cross symbol) formations, they have created a transitional zone between the two data sets (i.e., 18–20‰). Yellow points are from published data for the Devonian sulphate minerals (<a href="#geosciences-14-00224-t0A1" class="html-table">Table A1</a>). Concentrated pyrite samples (<span class="html-italic">n</span> = 3) have been separated from Montney Formation and are plotted as a grey dashed box and grey shaded. Concentrated kerogen samples (<span class="html-italic">n</span> = 5) are from the Montney Formation and are represented by the purple dashed box as the oxygen data are not necessarily associated with the organic sulphur and therefore not included in the plot. The isotopic sulphur data from the H<sub>2</sub>S gas are also represented by dashed boxes (red = Montney H<sub>2</sub>S gas; blue = Devonian H<sub>2</sub>S gas) as the H<sub>2</sub>S gas has no oxygen data associated with the sulphur (<a href="#geosciences-14-00224-t002" class="html-table">Table 2</a>). Montney H<sub>2</sub>S gas is isotopically lighter (<sup>34</sup>S-depleted) compared to Devonian H<sub>2</sub>S gas and each data set divided by geological age reflects a similar isotopic range as the anhydrite mineral from their respective geological period. These results indicate the sulphur of the Montney H<sub>2</sub>S is most likely sourced from Triassic anhydrite or from a mixture of Triassic and Devonian anhydrite sources if <sup>34</sup>S-enriched (i.e., δ<sup>34</sup>S is 14–21‰ V-CDT). Isotopic data for sulphate data are a combination of this study and from [<a href="#B15-geosciences-14-00224" class="html-bibr">15</a>,<a href="#B22-geosciences-14-00224" class="html-bibr">22</a>,<a href="#B23-geosciences-14-00224" class="html-bibr">23</a>]. Isotopic data for organic matter and pyrite are from this study (see <a href="#geosciences-14-00224-t0A1" class="html-table">Table A1</a>). Isotopic data for H<sub>2</sub>S gas are derived from this study and [<a href="#B11-geosciences-14-00224" class="html-bibr">11</a>,<a href="#B14-geosciences-14-00224" class="html-bibr">14</a>].</p>
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<p>Distribution of sulphur isotopic ratios (‰) in the Montney Formation on the base map of H<sub>2</sub>S concentration (%) distribution. Due to the H<sub>2</sub>S concentrations spanning orders of magnitude, the long-dashed contour intervals are at 1% with short-dashed contour intervals at 0.1%. The sulphur isotopic ratio for H<sub>2</sub>S gas in the Montney Formation shows no trends across the study area and has less <sup>34</sup>S -enriched ratios (9 to 21‰) compared to the range of sulphur ratios from Devonian reservoirs (i.e., 14.0 and 26.0‰). There are also more wells across a larger geographic area that are sour in Alberta than in British Columbia.</p>
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<p>The sulphur isotopic ratio range for the H<sub>2</sub>S gas subdivided into the upper, middle, and lower Montney Formation from this study (<span class="html-italic">n</span> = 60). The box-and-whisker plot indicates that there is no significant difference between the isotopic signatures of these informal units. See <a href="#geosciences-14-00224-t002" class="html-table">Table 2</a> for details on samples.</p>
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<p>Scanning electron microscope (SEM) images and energy dispersive X-ray spectroscopic (EDX) mapping showing the textural relationship between dolomite (Do) and anhydrite (An) within the upper Montney Formation. Well Authorization #30876 EDX maps show the concentration of magnesium (Mg; (<b>C</b>,<b>G</b>)) and calcium (Ca; (<b>B</b>)) in the dolomite minerals (Do; (<b>A</b>)) and the concentration of sulphur (S; (<b>D</b>,<b>F</b>)) and calcium (Ca; (<b>H</b>)) in the anhydrite minerals. The textural relationship shows the replacement of dolomite by anhydrite as either a fracture fill or diagenetic cement, infilling around dolomite grains (black arrow; (<b>E</b>)).</p>
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<p>Mineralogical textures in the overlying Charlie Lake Formation. The Charlie Lake Formation shows both detrital anhydrite grains (An; (<b>A</b>)) and evaporitic textures (An; (<b>E</b>)). The detrital anhydrite grains are also associated with quartz grains (Qz; (<b>A</b>)). The anhydrite is determined by the high calcium (Ca; <b>B</b>,<b>F</b>) and high sulphur (<b>D</b>,<b>H</b>) contents. The evaporitic texture seen in (<b>E</b>) is similar to the nodular texture seen in evaporites (i.e., [<a href="#B62-geosciences-14-00224" class="html-bibr">62</a>]); dolomite anhydrite associations are interpreted as evaporites in the Charlie Lake Formation [<a href="#B32-geosciences-14-00224" class="html-bibr">32</a>]. Very large dolomite grains also form in the evaporitic environment (<b>E</b>). The dolomite contains high magnesium (Mg; <b>C</b>,<b>G</b>) and calcium (Ca; <b>B</b>,<b>F</b>) contents. Well Authorization #30876.</p>
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<p>The Doig Formation that overlays the Montney Formation shows phosphatic nodules that are also rich in anhydrite (An), as illustrated by the high sulphur concentrations. The nodules are concentrated in calcium (Ca; (<b>C</b>,<b>G</b>)), sulphur (S; <b>B</b>,<b>F</b>), and phosphate (P; <b>D</b>,<b>H</b>)and are found associated with dolomite grains (Do in (<b>A</b>,<b>E</b>)). Anhydrite-rich phosphatic nodules have also been observed in the Doig Formation by [<a href="#B33-geosciences-14-00224" class="html-bibr">33</a>]. This detrital texture for the anhydrite shows that the sulphate would need to be dissolved from the nodules and then migrated and deposited in the Montney Formation as a cement in fractures.</p>
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<p>H<sub>2</sub>S formation models for sour Montney Formation wells in BC. A total of 218 sulphur sub-samples from mineral and organic matter samples and 120 sulphur subsamples from H<sub>2</sub>S gas were isotopically analysed. Diamond symbols represent the sulphur in the mineral form, prior to being converted to sulphate ions and used in the formation of H<sub>2</sub>S gas when in contact with hydrocarbons. Circle symbols represent the sulphur once converted to H<sub>2</sub>S gas. Model 1 represents an in situ conversion which would be the conversion of syn-depositional anhydrite that is derived from the Montney Formation. This model is plausible as the Montney Formation does contain small amounts of anhydrite (&lt;5%). However, the H<sub>2</sub>S distribution would be more consistent and the localised changes between sour and sweet lateral wells on the same pad (i.e., <a href="#geosciences-14-00224-f002" class="html-fig">Figure 2</a>) would not likely be observed. Model 2 is structurally controlled, with anhydrite or sulphate ions derived from sulphate minerals with the Triassic Charlie Lake Formation migrating through local fracture/fault systems into the Montney Formation (prior to hydrocarbon charging and overpressuring). H<sub>2</sub>S gas is generated once hydrocarbons are generated in the Montney Formation and the hydrocarbons react with the sulphate ions via the TSR pathway. A similar model has been shown for the migration of sulphate ions from the Charlie Lake and into the Halfway Formation [<a href="#B38-geosciences-14-00224" class="html-bibr">38</a>]. Model 3 is a mixing of sulphur ions from Triassic and Devonian sources and structural controls would need to include a deeper connection via structural features with the Devonian source. This results in a more <sup>34</sup>S-enriched isotopic signature in the H<sub>2</sub>S compared to Model 2. Ref. [<a href="#B14-geosciences-14-00224" class="html-bibr">14</a>] shows evidence that dissolved sulphate ions have migrated from the Devonian into the Montney Formation within the Alberta Montney play area.</p>
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19 pages, 17463 KiB  
Article
Impact Assessment of Digital Elevation Model (DEM) Resolution on Drainage System Extraction and the Evaluation of Mass Movement Hazards in the Upper Catchment
by Ahmad Qasim Akbar, Yasuhiro Mitani, Ryunosuke Nakanishi, Ibrahim Djamaluddin and Takumi Sugahara
Geosciences 2024, 14(8), 223; https://doi.org/10.3390/geosciences14080223 - 21 Aug 2024
Viewed by 388
Abstract
Worldwide, landslides claim many lives each year, with an average of 162.6 deaths reported in Japan from 1945 to 2019. There is growing concern about a potential increase in this number due to climate change. The primary source of shallow and rapid landslides [...] Read more.
Worldwide, landslides claim many lives each year, with an average of 162.6 deaths reported in Japan from 1945 to 2019. There is growing concern about a potential increase in this number due to climate change. The primary source of shallow and rapid landslides within watersheds is the 0-order basins, which are located above the 1st order drainage system. These active geomorphological locations govern the frequency of mass movement. Despite the recognition of their importance, little attention has been paid to the role of 0-order basins in initiating landslides. Drainage systems can be extracted using the Digital Elevation Model (DEM) in GIS software. However, the effect of DEM resolution on the extraction of 1st order basins remains unexplained. This research develops an algorithm to assess the impact of DEM resolution on the extraction of first-order basins, channel head points, and the identification of approximate 0-order basins. The study includes algorithms to evaluate the correlation between DEM resolution and 1st order drainage system extraction using fuzzy classification techniques for approximate 0-order basins. The algorithm was applied in Toho Village, Fukuoka, Japan, defining the most appropriate DEM and stream definition threshold with an 86.48% accuracy and ±30 m error margin for channel head points. Critical slip surfaces were identified inside the 0-order basins and validated with a landslide inventory map with a 91% accuracy. The developed algorithms support hazard management and land use planning, providing valuable tools for sustainable development. Full article
(This article belongs to the Section Natural Hazards)
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Figure 1

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<p>Study area location map.</p>
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<p>Drainage system algorithm diagram.</p>
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<p>Approximate 0-order basin algorithm diagram.</p>
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<p>Approximate 0-order basin concept model.</p>
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<p>Workflow of random calculation center extraction in approximate 0-order basins.</p>
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<p>Random calculation center extraction positions inside 0-order basin.</p>
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<p>(<b>a</b>) Diagram of an ellipsoidal slip surface showing the sliding direction, center, and elements. (<b>b</b>) Representation of the sliding mass along the ellipsoidal slip surface, with the ground surface and the axes.</p>
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<p>3D Monte Carlo slope stability analysis.</p>
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<p>Schematic of drainage system.</p>
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<p>Visual interpretation of channel head points.</p>
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<p>Results of the algorithm.</p>
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<p>Otoishi River catchment drainage system and approximate 0-order basin.</p>
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<p>(<b>a</b>) 1:200,000 scale geological map of the Otoishi River catchment. (<b>b</b>) Location map of the study area with an overview of the surrounding cities.</p>
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<p>Results of critical slip surface calculation.</p>
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19 pages, 11916 KiB  
Article
Ground Penetrating Radar (GPR) Investigations in Urban Areas Affected by Gravity-Driven Deformations
by Nicola Angelo Famiglietti, Pietro Miele, Bruno Massa, Antonino Memmolo, Raffaele Moschillo, Luigi Zarrilli and Annamaria Vicari
Geosciences 2024, 14(8), 222; https://doi.org/10.3390/geosciences14080222 - 20 Aug 2024
Viewed by 891
Abstract
The 1980 Ms 6.9 Irpinia earthquake was responsible for the activation or reactivation of numerous gravitative deformations mainly hosted by clayey lithotypes, affecting wide areas of Benevento Province and the Sele and Ofanto R. Valleys. The case of Calitri offers valuable insights into [...] Read more.
The 1980 Ms 6.9 Irpinia earthquake was responsible for the activation or reactivation of numerous gravitative deformations mainly hosted by clayey lithotypes, affecting wide areas of Benevento Province and the Sele and Ofanto R. Valleys. The case of Calitri offers valuable insights into a methodological approach to studying mass movements affecting human settlements. Post-earthquake investigations in Calitri involved extensive geognostic boreholes and in situ surveys, providing substantial data for lithological characterization and landslide modeling. Additionally, over the past two decades, satellite-based techniques have supported the mapping and characterization of ground deformations in this area, improving our understanding of spatiotemporal evolution. Despite these efforts, a detailed subsurface comprehensionof the tectono-stratigraphy and geometriesof gravity-induced deformation remains incomplete. This study aims to enhance our knowledge of gravity-driven deformations affecting urban areas by using deep-penetrating GroundPenetrating Radar (GPR) surveys to identify landslide-related structures, rupture surfaces, and lithological characterization of the involved lithotypes. The integration of GPR surveys with classical morphotectonic analysis led to the delineation of the main subsurface discontinuities (stratigraphy, tectonics, and gravity-related), correlating them with available geognostic data. This approach provided non-invasive, detailed insights into subsurface features and stands out as one of the rare case studies in Italy that employed the GPR method for landslide investigations. Full article
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Figure 1
<p>Topographic basemap 1:5000 (CTR Campania) with a simplified lithological map extracted from the CARG project—Sheet 451 “Melfi”. On the right side is the thickness of the outcropping formation according to literature: PDO—Paola Doce Formation; AV—Varicolored Clays; SAD—Vallicella Formation; RVM—Ruvo del Monte Synthem; UINa—slope deposits; UINb—alluvial deposit. The landslide limits were assumed from the Official Italian Landslide Inventory Database (IFFI), updated as of 2006.</p>
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<p>Map of the study area showing the locations of the boreholes considered and the GPR investigation tracks. The different colors of the borehole markers refer to the various geognostic investigations carried out in recent decades in many research projects. Base map is derived from a 1:5000 digital topographic map (Carta Tecnica Regionale, Regione Campania, year 2005).</p>
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<p>The flowchart resumes the approach adopted for this study and describes how the ancillary data were used for the correct interpretation of GPR profiles.</p>
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<p>Morphotectonic map derived from aerial photo-interpretation. Ofanto River-related morphologies: T1–T4, first- to fourth-order fluvial terraces, erosional glacis, and paleosurfaces. Gravity-related morphologies: landslide piles up to fourth generation; blue line, first-generation crowns of rototranslational slides; red lines, main tectonic lineaments; A-A’ brown dashed line, track of the elevation profile at the top-inset.</p>
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<p>GPR-1 radargram acquired in the Piazzale Giolitti parking area. Blue lines are main reflectors of contact between landslide piles and local substratum (more or less undisturbed). The cyan lines represent the possible emersion of rupture surfaces at the main scarps of the retrieved landslides. In the middle part of the radargram, considerable distortions affect the resolution due to the presence of subsurface utilities. On the left are the lithologies according to the S1 borehole: (A) fillings and altered clayey silt (RVMa); (B) brownish clayey silt with portions of crumbled limestone and sandstone; (C) compact gray silty clay (SAD2).</p>
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<p>(<b>A</b>) Three-dimensional GPR survey acquisition scheme performed at Piazzale Giolitti. The white arrows indicate evidence of surface rupture; (<b>B</b>) 2D model reconstruction; (<b>C</b>) 3D model reconstruction. The white arrows indicate visible cracks in the pavement. The colored depth scale refers to the separation landslide surface reconstructed through IDW interpolation of the depth data derived from the GPR 3D dataset (blue = 0 m, red = −10 m below ground). Data were post-processed in Geolitix environment (© 2024 Geolitix Technologies Inc., Vancouver, BC, Canada).</p>
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<p>A GPR-2 radargram acquired along Via De Sanctis. In correspondence with the slope break, substantial interruption in the reflector continuity can be noted. The final part of the profile contains other strong signals associated with the latest landslide reactivation, involving the southern part of the town. The blue lines represent the main reflectors of the contact between landslide piles and local substratum (more or less undisturbed). The cyan lines represent the possible emersion of rupture surfaces at the main scarps of the retrieved landslides. Below, photos (<b>A</b>,<b>B</b>) (by Luigi Toglia) were acquired in the days following the 1980 earthquake in the sectors identified by the red boxes in the upper radargram.</p>
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<p>GPR-3c radargram acquired in the eastern sector of the investigated area. Brown knurled line, paraconformity contact between RMVa and SAD2; light blue, sedimentary and minor geological contacts; yellow hatch, T4-order fluvial deposits; blue hatch, L3 Landslide toe pile.</p>
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<p>GPR-3b radargram sensed in the middle part of the portion at the base of the hill: in this sector the differences between the 3rd and 4th landslide generations can be noted. The presence of a buried drainage pipe (green circle) caused a deep disturbance in the central part of the section. Blue lines are main reflectors of contact between landslide piles and local substratum (more or less undisturbed). The cyan lines represent the possible emersion of surfaces of rupture at the main scarps of the retrieved landslides.</p>
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<p>GPR-3a radargram. The 3rd-order landslide pile continuously outcrops in the sector at the toe of Calitri Hill. The presence of buried drainage pipes (green circle) produced deep disturbances in the western part of the section.</p>
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15 pages, 4796 KiB  
Article
Risk Assessment of Nephrotoxic Metals in Soil and Water in Areas with High Prevalence of Chronic Kidney Disease in Panama
by Benedicto Valdés-Rodríguez, Virginia Montero-Campos, Matthew G. Siebecker, Amanda Jo Zimmerman, Mauricio Vega-Araya, Sharon P. Ulate Chacón and Dalys Rovira
Geosciences 2024, 14(8), 221; https://doi.org/10.3390/geosciences14080221 - 20 Aug 2024
Viewed by 463
Abstract
Mesoamerican nephropathy (MeN) is a non-traditional chronic kidney disease in some areas of Mesoamerica. The health risk from nephrotoxic metals, such as arsenic (As), lead (Pb), mercury (Hg), vanadium (V), cadmium (Cd), rubidium (Rb), chromium (Cr), and nickel (Ni), was assessed in drinking [...] Read more.
Mesoamerican nephropathy (MeN) is a non-traditional chronic kidney disease in some areas of Mesoamerica. The health risk from nephrotoxic metals, such as arsenic (As), lead (Pb), mercury (Hg), vanadium (V), cadmium (Cd), rubidium (Rb), chromium (Cr), and nickel (Ni), was assessed in drinking water and soils. These metals, even at low concentrations, have the capacity to induce epigenetic damage and a nephrotoxic effect. The quantification of metals in soils was made through X-ray fluorescence spectrometry (XRF) and inductively coupled plasma optical emission spectrophotometry (ICP-OES), while the quantification of metals in water was carried out through inductively coupled plasma mass spectrometry (ICPMS) and atomic absorption (AA) spectrometry. The levels of As, Hg, Cd, and V in water were within the permissible limits, whereas Pb was found to be double and triple the value recommended by the World Health Organization. The non-carcinogenic risk from As in soil was evaluated using the Hazard Index (HI), and the route of ingestion was found to be the most important route. The results indicate that consuming water or ingesting soil particles with Pb and As poses a health risk to humans. Therefore, these findings identify the presence of toxicants in an exposure scenario and justify further research into these metals in people and the analysis of exposure routes. Full article
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<p>Map of the study area soil (red circle, <span style="color:red">●</span>) and water (blue triangle <span style="color:#0070C0">▲</span>) sample collection sites in Coclé, Panama: MASL, meters above sea level.</p>
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<p>Nephrotoxic metals by districts in Coclé. (<b>A</b>) concentration of As; (<b>B</b>) concentration of Pb; (<b>C</b>) concentration of Cr; (<b>D</b>) concentration of V; (<b>E</b>) concentration of Rb; (<b>F</b>) concentration of Ni. Blue circles represent the average value for the series of data. The red dashed line indicates the 95th percentile of metal reported in soils (Smith et al., 2019) [<a href="#B36-geosciences-14-00221" class="html-bibr">36</a>]. The solid red line indicates the guide value of the chemical elements in the continental crust (Taylor, 1964) [<a href="#B38-geosciences-14-00221" class="html-bibr">38</a>].</p>
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<p>Geographical distribution of nephrotoxic metals in soils in Coclé. Represents the spatial distribution of metals as interpolated through ArcGIS: (<b>A</b>) distribution of As; (<b>B</b>) distribution of Pb; (<b>C</b>) distribution of Cr; (<b>D</b>) distribution of V; (<b>E</b>) distribution of Rb; (<b>F</b>) distribution of Ni. All results are expressed in mg kg<sup>−1</sup>.</p>
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<p>Geographical distribution morbidity to CKD and metals concentrations in Coclé, Panama. Data Source: Prevalence of CKD: Ministry of Panama Health [<a href="#B20-geosciences-14-00221" class="html-bibr">20</a>], metals concentration: Environmental Soil Chemistry Lab Texas Tech University, USA. Map created by ArGis Software (version 10.8.2).</p>
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23 pages, 2549 KiB  
Article
A Machine Learning-Driven Approach to Uncover the Influencing Factors Resulting in Soil Mass Displacement
by Apostolos Parasyris, Lina Stankovic and Vladimir Stankovic
Geosciences 2024, 14(8), 220; https://doi.org/10.3390/geosciences14080220 - 18 Aug 2024
Viewed by 439
Abstract
For most landslides, several destabilising processes act simultaneously, leading to relative sliding along the soil or rock mass surface over time. A number of machine learning approaches have been proposed recently for accurate relative and cumulative landside displacement prediction, but researchers have limited [...] Read more.
For most landslides, several destabilising processes act simultaneously, leading to relative sliding along the soil or rock mass surface over time. A number of machine learning approaches have been proposed recently for accurate relative and cumulative landside displacement prediction, but researchers have limited their studies to only a few indicators of displacement. Determining which influencing factors are the most important in predicting different stages of failure is an ongoing challenge due to the many influencing factors and their inter-relationships. In this study, we take a data-driven approach to explore correlations between various influencing factors triggering slope movement to perform dimensionality reduction, then feature selection and extraction to identify which measured factors have the strongest influence in predicting slope movements via a supervised regression approach. Further, through hierarchical clustering of the aforementioned selected features, we identify distinct types of displacement. By selecting only the most effective measurands, this in turn informs the subset of sensors needed for deployment on slopes prone to failure to predict imminent failures. Visualisation of the important features garnered from correlation analysis and feature selection in relation to displacement show that no one feature can be effectively used in isolation to predict and characterise types of displacement. In particular, analysis of 18 different sensors on the active and heavily instrumented Hollin Hill Landslide Observatory in the north west UK, which is several hundred metres wide and extends two hundred metres downslope, indicates that precipitation, atmospheric pressure and soil moisture should be considered jointly to provide accurate landslide prediction. Additionally, we show that the above features from Random Forest-embedded feature selection and Variational Inflation Factor features (Soil heat flux, Net radiation, Wind Speed and Precipitation) are effective in characterising intermittent and explosive displacement. Full article
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<p>Displacement recordings transformed to absolute plane vectorial displacements (x-coordinate + y-coordinate) for eastern and western lobes of the slope.</p>
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<p>Heat map of correlation values between pairs of influencing factors. The color bars’ values on the right side of the Figure indicate how strongly the factors are correlated.</p>
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<p>A 2D representation with 2 LDA components of 5 patterns of failure movement [<a href="#B28-geosciences-14-00220" class="html-bibr">28</a>].</p>
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<p>Left to right: Lasso feature importance ranking, RF regression feature importance ranking and XGBoost regression feature importance ranking, with respect to relative displacement.</p>
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<p>Horizontal axis on all the graphs shows time [days], while vertical shows cumulative displacement [mm]. Split ratio 70/30 (<b>left</b>); split ratio 50/50 (<b>right</b>). Lasso 1st row, RF 2nd row and XGBoost 3rd row are presented for recorded–predicted cumulative displacement on various time windows (1 d, 5 d, 10 d, 15 d, 30 d).</p>
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<p>Horizontal axis on all the graphs shows time [days], while vertical shows cumulative displacement [mm]. Split ratio 70/30 (<b>left</b>); split ratio 50/50 (<b>right</b>). Lasso 1st row, RF 2nd row and XGBoost 3rd row are presented for recorded–predicted cumulative displacement on various time windows (1 d, 5 d, 10 d, 15 d, 30 d).</p>
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<p>Clustering of data points to explain daily differential displacement, according to features selected by VIF (<b>left</b>) and RF (<b>right</b>): dendrogram with thresholds at 12.5 and at 8, as derived in <a href="#sec5-geosciences-14-00220" class="html-sec">Section 5</a>.</p>
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<p>Clustering of data points according to features selected by VIF (<b>left</b>) and RF (<b>right</b>): Daily differential displacement [mmday<sup>−1</sup>] vs. time [days]. Red indicates clustered data points related to major explosive movements while black indicates points related to intermittent movements.</p>
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<p>Clustering of data points to explain daily differential displacement, according to features (unitless, normalised per absolute maximum) selected by VIF (<b>top</b>) and RF (<b>bottom</b>): selected features vs. time, from left to right PRECIP, RN, G1, G2 and WS (<b>top</b>); PRECIP, PA, TDT1VWC, and TDT2VWC (<b>bottom</b>). Red indicates clustered data points related to major explosive movements while black indicates points related to intermittent movements.</p>
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<p>Clustering of data points to explain daily differential displacement, according to features (unitless, normalised per absolute maximum) selected by VIF (<b>top</b>) and RF (<b>bottom</b>): selected features vs. relative displacement, from left to right. Top: PRECIP, RN, G1, G2 and WS; Bottom: PRECIP, PA, TDT1VWC, and TDT2VWC. Red indicates clustered data points related to major explosive movements while black indicates points related to intermittent movements.</p>
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21 pages, 20051 KiB  
Review
Makran Subduction Zone: A Review and Synthesis
by Peyman Namdarsehat, Wojciech Milczarek, Seyed-Hani Motavalli-Anbaran and Matin Khaledzadeh
Geosciences 2024, 14(8), 219; https://doi.org/10.3390/geosciences14080219 - 18 Aug 2024
Viewed by 697
Abstract
This review synthesizes existing research to elucidate the factors driving the distinct tectonic behaviors in the western and eastern Makran subduction zone, focusing on seismic activity, uplift rate, convergence rate, coupling, and subduction angle. The literature identifies the asymmetry in pressure and the [...] Read more.
This review synthesizes existing research to elucidate the factors driving the distinct tectonic behaviors in the western and eastern Makran subduction zone, focusing on seismic activity, uplift rate, convergence rate, coupling, and subduction angle. The literature identifies the asymmetry in pressure and the variation in subduction angles between the western and eastern parts of the Makran as key factors in defining the region’s tectonic patterns. The western region has a steeper subduction angle, resulting in lower pressure, reduced coupling, and decreased seismic activity. This disparity arises from different interactions between the subducted and overriding plates. This article offers an overview of the Makran subduction zone, identifies some knowledge gaps, and suggests directions for future research to improve our understanding of this complex geological region. The review highlights the need for more comprehensive GPS stations and targeted studies on subduction dip angles to better understand the region’s tectonic dynamics. Full article
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<p>The study area and its structural features are shown, highlighting the Sistan Suture Zone, the Jazmurian Depression (Jazmurian D), the Mashkel Depression (Mashkel D), the Sonne Fault, the Ormara Microplate, and the Murray Ridge. Brown triangles indicate the locations of volcanoes in the Bazman Group (BG), the Taftan Group (TG), and the Sultan Group (SG) [<a href="#B63-geosciences-14-00219" class="html-bibr">63</a>,<a href="#B64-geosciences-14-00219" class="html-bibr">64</a>,<a href="#B65-geosciences-14-00219" class="html-bibr">65</a>].</p>
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<p>Simplified tectonic map of the Makran region, showing the study area, and the main geological features. The map highlights the distinct boundaries of the Lut block, identified as the western (marked as No. 1) and eastern (marked as No. 2) boundaries, both marked by prominent dextral strike-slip fault systems. The Chaman fault (CF) (marked as No. 3), a sinistral strike-slip fault, is shown along with its rate of movement based on geomorphological measurements [<a href="#B73-geosciences-14-00219" class="html-bibr">73</a>]. The Ornach Nal sinistral strike-slip fault (OF) (marked as No. 4) is also shown. The MZP fault system (marked as No. 5) is displayed along with its corresponding rates of motion [<a href="#B41-geosciences-14-00219" class="html-bibr">41</a>]. The Main Zagros Thrust (MZT) is shown. The Jazmurian Depression (Jazmurian D), Mashkel Depression (Mashkel D), and Sistan Suture Zone are also indicated. Quaternary volcanoes, including the Bazman Group (BG), Taftan Group (TG), and Sultan Group (SG), are represented by brown triangles [<a href="#B63-geosciences-14-00219" class="html-bibr">63</a>,<a href="#B64-geosciences-14-00219" class="html-bibr">64</a>,<a href="#B65-geosciences-14-00219" class="html-bibr">65</a>]. In the Lut block, an orange symbol denotes a counterclockwise rotation, supported by GPS measurements and paleomagnetic interpretations [<a href="#B77-geosciences-14-00219" class="html-bibr">77</a>,<a href="#B78-geosciences-14-00219" class="html-bibr">78</a>]. Red circles represent GPS stations that visualize the velocities of movement associated with the convergence between the Arabian and Eurasian plates [<a href="#B41-geosciences-14-00219" class="html-bibr">41</a>]. The white arrows show the rate of motion of the Arabian plate relative to the Eurasian plate according to the GEODVEL-2010 model [<a href="#B56-geosciences-14-00219" class="html-bibr">56</a>]. The white rectangle indicates the study area analyzed for the focal mechanisms of earthquakes in Makran (<a href="#geosciences-14-00219-f003" class="html-fig">Figure 3</a>) by Penney et al. [<a href="#B22-geosciences-14-00219" class="html-bibr">22</a>].</p>
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<p>(<b>a</b>) shows earthquakes with a magnitude (M<sub>b</sub>) greater than 4 from 1945 to 2013. Semi-transparent events indicate poorly constrained depths and/or mechanisms. (<b>b</b>) displays earthquakes with a magnitude (M<sub>b</sub>) greater than 4 from 1945 to 2013 with well-constrained depths. The white triangles indicate the locations of seismometers CHBR and TURB. The figure presents structural boundaries, faults, and the locations of significant earthquakes that ruptured the plate boundary in the eastern part in 1756, 1851, and 1945, as well as a probable historical earthquake of magnitude M<sub>w</sub> 7.7 in 1483 in western Makran. The brown triangles represent the symbols for the Bazman Group (BG), Taftan Group (TG), and Sultan Group (SG) volcanoes. The green rectangles indicate the structural boundaries of the Makran: the MZP (marked as No. 1), the boundary between western and eastern Makran (marked as No. 2), and the OF and CF (marked as No. 3) (modified after Penney et al. [<a href="#B21-geosciences-14-00219" class="html-bibr">21</a>]).</p>
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<p>Background seismicity includes earthquakes from the ISC catalog from 1916 to 2015. These are categorized into deep (≥33 km; dark circles) and shallow (&lt;33 km; light brown circles) events, with ISC’s depth errors considered. Light green vectors represent velocity vectors based on the NUVEL-1 model [<a href="#B98-geosciences-14-00219" class="html-bibr">98</a>]. Subduction fronts SF1 (western part) and SF2 (eastern part) are marked with yellow ellipses. A zone of shallow and intermediate-depth earthquakes, approximately 250 km long with an ENE orientation, is present north of SF2 in eastern Makran, highlighted by a gray ellipse (lineament of earthquakes). SF1 and SF2 areas may have ESE and ENE orientations, respectively, with SF2 parallel to the gray ellipse. Mud volcanoes along the coast are marked by yellow stars. White ellipses represent the SW and NE elongations of volcanic distributions in the western and eastern regions, respectively. The brown triangles represent the symbols for the Bazman Group (BG), Taftan Group (TG), and Sultan Group (SG) volcanoes. Brown face-to-face polygons (possible coupling areas) and yellow ellipses (subduction fronts) divided by a solid red line indicate a NNW–SSE continental left-lateral fracture zone (FZ) connecting the Sistan Suture Zone (SSZ) and the Sonne Fault (SOF) (modified after Nemati [<a href="#B8-geosciences-14-00219" class="html-bibr">8</a>]).</p>
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<p>Red circles indicate the locations of events from June 2016 to November 2019 used to calculate the seismicity parameter (M<sub>L</sub> &lt; 3.3 and depth &lt; 10 km). The pink line indicates the western border of the broad boundary between western and eastern Makran. Beachballs represent three strike-slip events (modified after Akbarzadeh Aghdam et al. [<a href="#B28-geosciences-14-00219" class="html-bibr">28</a>]).</p>
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<p>The elevation of marine terraces and platforms along the Makran coast is based on data from Snead [<a href="#B34-geosciences-14-00219" class="html-bibr">34</a>].</p>
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<p>Horizontal velocities of geodetic benchmarks in the reference frame of stable Eurasia [<a href="#B106-geosciences-14-00219" class="html-bibr">106</a>], including 95% confidence ellipses [<a href="#B22-geosciences-14-00219" class="html-bibr">22</a>,<a href="#B45-geosciences-14-00219" class="html-bibr">45</a>,<a href="#B46-geosciences-14-00219" class="html-bibr">46</a>,<a href="#B107-geosciences-14-00219" class="html-bibr">107</a>] (modified after Ghadimi et al. [<a href="#B23-geosciences-14-00219" class="html-bibr">23</a>]).</p>
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<p>GPS velocity field relative to the Eurasian fixed frame [<a href="#B22-geosciences-14-00219" class="html-bibr">22</a>,<a href="#B58-geosciences-14-00219" class="html-bibr">58</a>]. The brown triangles represent symbols for the Bazman Group (BG), Taftan Group (TG), and Sultan Group (SG) volcanoes (modified after Abbasi et al. [<a href="#B58-geosciences-14-00219" class="html-bibr">58</a>]).</p>
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<p>The coupling ratio of the Makran subduction megathrust fault is depicted on a topographic map. The circles and ellipses indicate discrepancies between observed and modeled GPS velocity vectors. The small labels identify GPS stations, and dashed lines represent free-slip boundaries [<a href="#B58-geosciences-14-00219" class="html-bibr">58</a>].</p>
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<p>Convergence rate of the Arabian plate with respect to the Eurasian plate from west to east. The estimates are derived from various plate motion models [<a href="#B47-geosciences-14-00219" class="html-bibr">47</a>,<a href="#B48-geosciences-14-00219" class="html-bibr">48</a>,<a href="#B49-geosciences-14-00219" class="html-bibr">49</a>,<a href="#B50-geosciences-14-00219" class="html-bibr">50</a>,<a href="#B51-geosciences-14-00219" class="html-bibr">51</a>,<a href="#B52-geosciences-14-00219" class="html-bibr">52</a>,<a href="#B53-geosciences-14-00219" class="html-bibr">53</a>,<a href="#B54-geosciences-14-00219" class="html-bibr">54</a>,<a href="#B55-geosciences-14-00219" class="html-bibr">55</a>,<a href="#B56-geosciences-14-00219" class="html-bibr">56</a>,<a href="#B57-geosciences-14-00219" class="html-bibr">57</a>]. Data obtained from the UNAVCO plate motion calculator are accessible at <a href="https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html" target="_blank">https://www.unavco.org/software/geodetic-utilities/plate-motion-calculator/plate-motion-calculator.html</a>, accessed on 15 June 2014.</p>
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<p>Shows the maximum horizontal compressional stress, which was determined through focal mechanism stress inversion. The stress state varies from the western to the eastern Makran regions. In the western Makran, the stress field is impacted by the collision between Arabia and Eurasia, while in the eastern Makran, it is influenced by the Indian–Eurasian stress field. The brown triangles represent symbols for the Bazman Group (BG), Taftan Group (TG), and Sultan Group (SG) volcanoes [<a href="#B16-geosciences-14-00219" class="html-bibr">16</a>].</p>
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<p>Schematic model of subduction in the Makran region with two different dip angles. The yellow and green arrows indicate increasing velocity convergence from west to east, highlighting the progressive intensification of plate motion. The blue dashed line marks the prominent boundary between two subducting plates characterized by different dip angles. The yellow triangles represent the symbols for volcanoes.</p>
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32 pages, 83957 KiB  
Article
Stealth Metasomatism in Granulites from Ivrea (NW Italy): Hydration of the (Variscan) Lower Crust by Melt Flow
by Stylianos Karastergios, Simona Ferrando, Barbara E. Kunz and Maria Luce Frezzotti
Geosciences 2024, 14(8), 218; https://doi.org/10.3390/geosciences14080218 - 16 Aug 2024
Viewed by 378
Abstract
Granulites and associated dykes from the less well-studied southern Ivrea–Verbano Zone (around Ivrea town) are characterized by combining field, macro, micro and chemical (major and trace-element mineral composition) data to identify chemical and rheological variations in the lower crust that could be relevant [...] Read more.
Granulites and associated dykes from the less well-studied southern Ivrea–Verbano Zone (around Ivrea town) are characterized by combining field, macro, micro and chemical (major and trace-element mineral composition) data to identify chemical and rheological variations in the lower crust that could be relevant for geodynamic implications. The Ivrea granulites are similar to those in the Lower Mafic Complex of the central Ivrea–Verbano Zone. The mafic lithologies experienced stealth metasomatism (pargasitic amphibole and An-rich plagioclase) that occurred, at suprasolidus conditions, by a pervasive reactive porous flow of mantle-derived orogenic (hydrous) basaltic melts infiltrated along, relatively few, deformation-assisted channels. The chemical composition of the metasomatic melts is similar to that of melts infiltrating the central and northern Ivrea–Verbano Zone. This widespread metasomatism, inducing a massive regional hydration of the lowermost Southalpine mafic crust, promoted a plastic behavior in the lowermost part of the crust during the Early Mesozoic and, ultimately, the Triassic extension of the Variscan crust and the beginning of the Alpine cycle. Full article
(This article belongs to the Section Geochemistry)
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<p>Simplified tectonic map of the western Southern Alps, Ivrea–Verbano Zone (IVZ); modified after [<a href="#B51-geosciences-14-00218" class="html-bibr">51</a>]. The red rectangle represents the investigated area. Abbreviations: CMB = Cossato-Mergozzo-Brissago; PL = Pogallo Line; CL = Cremosina Line.</p>
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<p>Simplified geological map of the studied area. ICL = Intenral Canavese Line; ECL = External Canavese Line.</p>
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<p>Mafic granulite field appearance. (<b>A</b>) Typical medium- to coarse-grained granoblastic mafic granulite mainly consisting of Pl and Amp in similar modal proportion. Locality: Ivrea market. (<b>B</b>) Coarse-grained faintly foliated mafic granulite in which a green, with a grayish hue, clinopyroxene (Cpx), is surrounded by black Amp. The foliation is locally marked by elongated Amp (black arrow). Locality: Ivrea, Beneficio di S. Lucia road. (<b>C</b>) Coarse-grained granoblastic mafic granulite showing dismembered Cpx-rich layers (arrows). The selvages blend gradually into the granulites or are marked by Pl. Locality: Ivrea, Bric Appareglio. (<b>D</b>) Detail of a Cpx-layer within medium-grained granoblastic mafic granulite. The layer is cut and infiltrated by coarse-grained Pl and minor Amp, and the selvages are marked by infiltrating Pl. Locality: Ivrea, Bric Appareglio. The mineral abbreviations are after [<a href="#B78-geosciences-14-00218" class="html-bibr">78</a>].</p>
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<p>Enderbite field appearance. (<b>A</b>) Typical medium-grained granoblastic enderbite mainly consisting of Pl and orthopyroxene (Opx). Locality: Ivrea, Monte Marino Road. (<b>B</b>) Faintly foliated enderbite in which the foliation is marked by coarse-grained elongated Opx. Locality: Ivrea, Monte Marino Road. (<b>C</b>) Fine-grained enderbitic dykes within coarse-grained enderbite. Locality: Ivrea, Monte Marino Road. (<b>D</b>) Fine-grained enderbitic dyke within medium-grained mafic granulite. Locality: Ivrea, Monte Stella Road.</p>
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<p>Amp-rich dykes and pods field appearance. (<b>A</b>) Pegmatoid Amp-rich dyke crosscutting enderbite with straight contact. Locality: Ivrea, Monte Marino Road. (<b>B</b>) Coarse-grained Amp-rich pod gradually fading into mafic granulite. Locality: Ivrea, Bric Appareglio. (<b>C</b>) Coarse-grained hornblenditic lens within mafic granulite. Locality: Ivrea, Bric Appareglio. (<b>D</b>) Medium-grained hornblenditic dyke crosscutting the enderbite with a straight contact. Locality: Ivrea, Monte Stella Road.</p>
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<p>Field appearance of other, minor lithologies. (<b>A</b>) Coarse-grained quartz-felspathic dyke crosscutting enderbite with straight contact. Locality: Ivrea, Campagna Lake. The inset shows a close-up image of a quartz-feldspathic dyke crosscutting mafic granulites. Locality: Ivrea, Monte Stella Road. (<b>B</b>) Stronalite outcrop showing cm-wide porphyroblastic garnet and cm-thick quartz (Qz)-vein. The Qz grayish color is due to inclusions of rutile needles. Locality: Ivrea, Canton Gabriel Road. (<b>C</b>) Cm-thick Alpine vein consisting of green epidote + plagioclase + chlorite (Ep + Pl + Chl) within mafic granulite. Locality: Ivrea market.</p>
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<p>Mafic granulites under the microscope. (<b>A</b>) Scan (<b>right</b>) and panoramic photomicrograph (<b>left</b>) on plane-polarized light (PPL) of an Amp–Cpx-poor mafic granulite with a pyroxenitic band parallel to the rock foliation (sample IZ23-02b). Variations in grain size, in modal amounts of pyroxenes, and in amphibole are recognizable. (<b>B</b>) Photomicrograph showing porphyroclastic Pl and pyroxenes with lobate grain-boundaries in associations with smaller granoblastic neoblasts. Sample IZ23-02b, PPL. (<b>C</b>) Interstitial Pl (white arrow) filling the grain boundary between opaques (Opq) and Cpx. Sample IZ21-17, PPL. (<b>D</b>) Interstitial Pl on Mag and on rounded exsolution-rich Cpx. Sample IZ21-17, back-scattered electron image (BSE). (<b>E</b>) Interstitial Pl between pyroxenes and Hc or Mag, but not between Hc and Amp. Note the Opx exsolution lamellae within porphyroclastic Cpx. Sample IZ21-17, BSE. (<b>F</b>) Typical brown exsolutions within porphyroclastic Opx. Sample IZ21-20a, thick section, PPL. (<b>G</b>) Interstitial Amp between Pl-Pl and Opx-Cpx grain boundaries. Sample IZ21-20a, PPL (<b>H</b>) Rounded Cpx partly replaced by interstitial Amp. Sample IZ21-18a, PPL. (<b>I</b>) Granoblastic polygonal Amp with ilmenite ± magnetite (Ilm ± Mag) “beads” at the grain boundaries. Sample IZ21-18a, BSE. (<b>J</b>) Allotriomorphic Mag partly preserved from Hc substitution. Note the Pl continuous film at the contact between Cpx and Mag or Hc. Sample IZ21-17, BSE.</p>
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<p>Plagioclase chemical composition in mafic granulite and Amp–Cpx-poor granulite (<b>a</b>) and in enderbite (<b>b</b>).</p>
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<p>Orthopyroxene chemical composition in mafic granulite and Amp–Cpx-poor granulite (<b>a</b>,<b>b</b>) and in enderbite (<b>c</b>,<b>d</b>).</p>
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<p>Clinopyroxene chemical composition in mafic granulite and Amp–Cpx-poor granulite. Correlation of the #Mg with (<b>a</b>) Ca a.p.f.u., (<b>b</b>) Al a.p.f.u., (<b>c</b>) Ti a.p.f.u., and (<b>d</b>) Na a.p.f.u.</p>
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<p>Amphibole chemical composition in mafic granulite, Amp–Cpx-poor granulite and enderbite.</p>
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<p>Px-rich layers and Amp-rich dykes and pods under a microscope. (<b>A</b>) Typical Px-rich layer consisting of Cpx + Opx + Opq + Pl and showing a granoblastic structure with lobate grain-boundaries, usually evolving toward polygonal ones. Sample IZ23-02b, PPL. (<b>B</b>) Coarse-grained Pl and minor Amp cutting and infiltrating a Px-rich layer (for a macroscopic view, see <a href="#geosciences-14-00218-f003" class="html-fig">Figure 3</a>D above). Sample IZ21-15a, PPL. (<b>C</b>) Interstitial Amp surrounding Mag from a Px-rich layer. Sample IZ21-15a, PPL. (<b>D</b>) Interstitial Pl at the contact between Mag and Cpx from a Px-rich layer. Sample IZ21-15a, PPL. (<b>E</b>) Pegmatitic Amp from an Amp-rich pod (for a macroscopic view, see <a href="#geosciences-14-00218-f005" class="html-fig">Figure 5</a>B above) partly replacing Opx of a mafic granulite. Sample IZ21-17, PPL. (<b>F</b>) Pegmatitic polygonal Amp, from an Amp-rich pod, with opaque “beads” at the Amp–Amp grain boundaries. Sample IZ21-17, PPL.</p>
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<p>Enderbites under the microscope. (<b>A</b>,<b>B</b>) Enderbite showing a faint foliation defined by a dimensionally oriented porphyroclastic, exsolution-rich Opx (<b>A</b>), or by an elongated aggregate of neoblastic, exsolution-free Opx (<b>B</b>). Sample IZ21-04a, PPL. (<b>C</b>) Porphyroclastic Pl showing lobate grain-boundaries (red-dashed line) with exsolution-rich Opx. Sample IZ21-01, PPL. (<b>D</b>) Neoblastic Opx and Pl showing polygonal granoblastic microstructure. Note the rare green–brown Amp around Opq. Sample IZ21-04a, PPL. (<b>E</b>) Pl showing Qz blebs in their interior and at their grain boundaries. Note a change in Pl composition around the blebs. Sample IZ21-02, crossed polarized light (XPL). (<b>F</b>) Qz blebs associated with replacement antiperthitic Kfs and with an increase in anorthite component of the hosting Pl. Sample IZ21-01, BSE.</p>
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<p>Rare Earth Element (REE) and incompatible trace element patterns of the most abundant minerals in the studied mafic granulites of Ivrea town area: (<b>a</b>,<b>b</b>) clinopyroxene; (<b>c</b>,<b>d</b>) amphibole; (<b>e,f</b>) orthopyroxene and (<b>g</b>,<b>h</b>) plagioclase. The plots show individual analyses for each mineral. Primitive mantle and CI–chondrite values are from McDonough and Sun [<a href="#B80-geosciences-14-00218" class="html-bibr">80</a>].</p>
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<p>Thermobarometric data from Amp. (<b>a</b>) Histograms of the temperatures calculated using the equation of [<a href="#B81-geosciences-14-00218" class="html-bibr">81</a>]. (<b>b</b>) Composition of amphiboles plotted in the Si vs. Ti diagram. The field of high amphibolite (HAM), HT granulite (HT-GR), and UHT granulite facies are from [<a href="#B81-geosciences-14-00218" class="html-bibr">81</a>]. (<b>c</b>) Composition of amphiboles plotted in the Al<sup>IV</sup> vs. Al<sup>VI</sup> diagram. The distinction between low-P and high-P amphiboles (solid line) is from [<a href="#B84-geosciences-14-00218" class="html-bibr">84</a>], and that among amphibole composition from the different metamorphic facies is from [<a href="#B85-geosciences-14-00218" class="html-bibr">85</a>]: (1) greenschist facies; (2) albite–epidote amphibolite facies; (3) amphibolite facies; (4) granulite facies.</p>
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<p>Major element amphibole diagrams comparing this work with the dioritic and hornblenditic dykes of Ogunyele et al. [<a href="#B20-geosciences-14-00218" class="html-bibr">20</a>] from the Finero Mafic Complex.</p>
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<p>Rare Earth Element (REE) and incompatible trace element patterns of the most abundant minerals in the studied mafic granulites of Ivrea town area compared with previous studies from the Finero Mafic Complex [<a href="#B18-geosciences-14-00218" class="html-bibr">18</a>,<a href="#B19-geosciences-14-00218" class="html-bibr">19</a>,<a href="#B20-geosciences-14-00218" class="html-bibr">20</a>] and Monte Capio area [<a href="#B34-geosciences-14-00218" class="html-bibr">34</a>], where hornblenditic dykes and associated mafic granulites are reported to have reacted with hydrous basaltic melts. (<b>a</b>,<b>b</b>) Clinopyroxene; (<b>c</b>,<b>d</b>) amphibole; (<b>e</b>,<b>f</b>) orthopyroxene and (<b>g</b>,<b>h</b>) plagioclase. Primitive mantle and CI–chondrite values are from McDonough and Sun [<a href="#B80-geosciences-14-00218" class="html-bibr">80</a>].</p>
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<p>Tectono–thermal sketch of the late-Variscan extension (310–270 Ma) in the Alpine Tethys realm. The isotherm locations are approximate. Modified after Wyatt et al. [<a href="#B36-geosciences-14-00218" class="html-bibr">36</a>], Manatschal et al. [<a href="#B103-geosciences-14-00218" class="html-bibr">103</a>], Petri et al. [<a href="#B104-geosciences-14-00218" class="html-bibr">104</a>], and Real et al. [<a href="#B105-geosciences-14-00218" class="html-bibr">105</a>].</p>
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18 pages, 18205 KiB  
Article
Interpreting Soft-Sediment Deformation Structures: Insights into Earthquake History and Depositional Processes in the Dead Sea, Jordan
by Bety S. Al-Saqarat, Mahmoud Abbas, Mu’ayyad Al Hseinat, Tala Amer Qutishat, Duha Shammar and Ehab AlShamaileh
Geosciences 2024, 14(8), 217; https://doi.org/10.3390/geosciences14080217 - 16 Aug 2024
Viewed by 570
Abstract
Soft-sediment deformation structures (SSDSs) typically form in unconsolidated sedimentary deposits before lithification. Understanding these structures involves evaluating their characteristics, genesis timing, and the dynamics of sediment deformation. SSDSs are essential for deciphering ancient environments, reconstructing depositional processes, and discerning past prevailing conditions. In [...] Read more.
Soft-sediment deformation structures (SSDSs) typically form in unconsolidated sedimentary deposits before lithification. Understanding these structures involves evaluating their characteristics, genesis timing, and the dynamics of sediment deformation. SSDSs are essential for deciphering ancient environments, reconstructing depositional processes, and discerning past prevailing conditions. In the Dead Sea region, SSDSs are abundant and well preserved due to unique geological and environmental factors, including rapid sedimentation rates and seismic activity. Influenced by the Dead Sea Transform Fault, the area offers insights into tectonic activity and historical earthquakes predating modern instrumentation. This study extensively examines SSDSs along the Dead Sea area in Jordan, focusing on sediments near the Lisan Peninsula, where the prominent Lisan Formation (71–12 ka) exposes numerous deformations. Mineralogical and geochemical analyses using X-ray diffraction (XRD) and X-ray fluorescence (XRF) were applied on deformed and undeformed layers to test the potential trigger of seismite formation in the Dead Sea area. The XRD and XRF results reveal Aragonite and Halite as the predominant compounds. Field observations, coupled with mineralogical and geochemical data, suggest tectonic activity as the primary driver of SSDSs formation in the Dead Sea region. Other contributing factors, such as high salinity, arid climate, and depositional settings, may also have influenced their formation. These structures offer valuable insights into the region’s geological history, environmental conditions, and tectonic evolution. Full article
(This article belongs to the Section Sedimentology, Stratigraphy and Palaeontology)
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<p>(<b>A</b>) Location map of Jordan (dashed red line) exhibits the major structural elements, including the DSTF segments (based on [<a href="#B31-geosciences-14-00217" class="html-bibr">31</a>,<a href="#B32-geosciences-14-00217" class="html-bibr">32</a>]). The plate tectonic configuration of the region is modified from Stern and Johnson [<a href="#B33-geosciences-14-00217" class="html-bibr">33</a>]. DSF: Dead Sea Fault; JVF: Jordan Valley Fault; WAF: Wadi Araba Fault [<a href="#B31-geosciences-14-00217" class="html-bibr">31</a>,<a href="#B32-geosciences-14-00217" class="html-bibr">32</a>,<a href="#B33-geosciences-14-00217" class="html-bibr">33</a>]. (<b>B</b>) Satellite image showing the locations of the nineteen studied outcrops of SSDSs in the Dead Sea area.</p>
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<p>Stratigraphic column of the Lisan Formation in central Jordan (modified after Abed and Yagan [<a href="#B57-geosciences-14-00217" class="html-bibr">57</a>].</p>
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<p>(<b>A</b>) Two deformed beds with southward verging slumps of variable size (31°14′6.1″ N, 35°31′12.20″ E; site 15). (<b>B</b>) One of the deformed layers with westward verging slumps (31°14′6.10″ N, 35°31′12.20″ E; site 11). (<b>C</b>) Deformed layer between two undeformed layers (31°13′38.70″ N, 35°31′6.80″ E; site 15). (<b>D</b>) Two deformed layers separated by undeformed layer (31°14′5.03″ N, 35°31′14.97″ E; site 13). (<b>E</b>) Four deformed beds with slumps and with different thicknesses and wavelengths (31°14′5.03″ N, 35°31′14.97″ E; site 13). The length of the hammer is 41 cm, the pen is 11 cm, and the shovel is 22 cm.</p>
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<p>(<b>A</b>) Mixed layer contains diapir fragments of marl mixed with sandy layer (31°6′1.60″ N, 35°31′36.10″ E; site 4). (<b>B</b>) Mixed layer showing fragments of course- and fine-grained sediments (31°6′1.60″ N, 35°31′36.10″ E; site 4). The length of the hammer is 41 cm and of the Silva compass is 17 cm.</p>
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<p>(<b>A</b>) Neptunian dyke: the arrow indicates the direction of sediments flow from the top to the bottom as evidenced by decreased width of the dyke with depth (31°13′34.60″ N, 35°31′15.40″ E; site 10). (<b>B</b>) Injection dyke: the arrow indicates the direction of the flow of liquified sediments from bottom to the top of the stratigraphic section (31°14′6.10″ N, 35°31′12.20″ E; site 15). The length of the hammer is 33 cm.</p>
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<p>(<b>A</b>) Load and flame structures (31°6′4.10″ N, 35°31′37.80″ E; site 3). (<b>B</b>) Load structure (31°6′4.10″ N, 35°31′37.80″ E; site 3). The length of the hammer is 41 cm and of the pen is 14 cm.</p>
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<p>Folded laminated marl that incorporates a cap layer of fine to medium-grained sandstone (31°13′34.60″ N, 35°31′15.40″ E; site 10). The length of the pen is 14 cm.</p>
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<p>(<b>A</b>) XRD results for the major minerals in the undeformed layer (DST1). (<b>B</b>) XRD results for the major minerals in the deformed layer (DST2).</p>
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<p>(<b>A</b>) Thin section reveals 10× zoom to aragonite crystals in undeformed layer. (<b>B</b>) Thin section 10× zoom of aragonite crystals in deformed layer.</p>
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<p>(<b>A</b>) A transtensional fault system comprising several normal faults that intersect the Quaternary deposits (the Lisan Formation), forming a negative-flower structure. (<b>B</b>–<b>D</b>) (31°5′44.20″ N, 35°31′31.00″ E; site 1) and (31°13′35.10″ N, 35°31′28.00″ E; site 8).</p>
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<p>(<b>A</b>) Rose diagram showing the faults measurements and indicating the correspondence with the DSTF and the Karak Wadi Al Fayha Fault. (<b>B</b>) Cyclographs and poles (dots) of the faults (dots).</p>
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9 pages, 4907 KiB  
Brief Report
A Report of the Observed Intensity and Structural Damage during the Mw 5.3 Earthquake in Santo Domingo (Province of Chiriquí, Panamá) on 8 July 2024
by Luis A. Pinzón, Yessica Vargas and Diego A. Hidalgo-Leiva
Geosciences 2024, 14(8), 216; https://doi.org/10.3390/geosciences14080216 - 15 Aug 2024
Viewed by 409
Abstract
On 8 July 2024, a magnitude 5.3 earthquake struck the province of Chiriquí in Panama, primarily impacting areas characterized by informal settlements and low-income neighborhoods. The earthquake was recorded by both the Panama Accelerographic Network and the Costa Rican Strong Motion Network, with [...] Read more.
On 8 July 2024, a magnitude 5.3 earthquake struck the province of Chiriquí in Panama, primarily impacting areas characterized by informal settlements and low-income neighborhoods. The earthquake was recorded by both the Panama Accelerographic Network and the Costa Rican Strong Motion Network, with accelerations exceeding 150 cm/s2. The National Civil Protection System (SINAPROC) reported damage to 24 residences and public infrastructure, including hospitals and schools. Despite the material damage, no fatalities were reported. The Ministry of Housing and Land Management (MIVIOT), the Ministry of Education (MEDUCA), and the Ministry of Social Development (MIDES) also participated in the assessment and response efforts. This report presents the measurements and damage observed during the event. Full article
(This article belongs to the Section Natural Hazards)
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<p>Shakemap from the earthquake that occurred on 8 July 2024, by the Earthquake Engineering Laboratory. The event was identified as a local strike-slip fault, associated with the series of faults crossing the Gulf of Chiriquí, with a moment magnitude of 5.3. Scale is based on Worden et al. [<a href="#B20-geosciences-14-00216" class="html-bibr">20</a>].</p>
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<p>The attenuation of peak ground acceleration as a function of epicentral distance recorded at 95 stations from the Panama Accelerographic Network and the Costa Rican Strong Motion Network. CHUS refers to the seismic station located in David, Chiriquí, Panama.</p>
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<p>Accelerograms recorded at CHUS. The signals were baseline-corrected and filtered using a fourth-order Butterworth bandpass filter with a frequency range of 0.10 to 25 Hz.</p>
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<p>Peak ground acceleration as a function of the rotation angle recorded at CHUS.</p>
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<p>Comparison of the 5% damped response spectra estimated with RotD100 (rotated component of ground motion spectra that envelope all possible orientations), horizontal acceleration components (N-S and E-W), and rotated components (° rot) from the 8 July 2024 Mw 5.3 Santo Domingo earthquake recorded at CHUS.</p>
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<p>A comparison of the response spectra of records at CHUS during the 2024 Santo Domingo earthquake with those suggested in the Panama structural regulations (REP) for David (Chiriquí) in the 2004 (green line) and 2021 versions (red and blue lines). For 2021, the spectra corresponding to uniform seismic hazards with return periods of 2500 (red line) and 475 years (blue line), respectively, are shown. These spectra were developed for Site B (rock—amplification factor of 1) and an importance coefficient of 1.</p>
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<p>Structural damage observed after Santo Domingo earthquake: (<b>a</b>) Residence in Los Abanicos, (<b>b</b>) dwelling in El Muertito, and (<b>c</b>) residence in Puerto Armuelles.</p>
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<p>Structural damage observed following the Santo Domingo earthquake: (<b>a</b>) Las Vueltas School in San Lorenzo, (<b>b</b>,<b>c</b>) IPT Abel Tapiero Miranda in San Lorenzo, (<b>d</b>) the tower of the San José Cathedral in David, (<b>e</b>) stained-glass windows in commercial areas in David, and (<b>f</b>) suspended ceilings in public and private offices in David.</p>
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25 pages, 27110 KiB  
Case Report
Geomechanics and Geology of Marine Terraces of the Crotone Basin, Calabria (Italy)
by Gloria Campilongo, Maurizio Ponte, Francesco Muto, Salvatore Critelli, Filippo Catanzariti and Davide Milone
Geosciences 2024, 14(8), 215; https://doi.org/10.3390/geosciences14080215 - 13 Aug 2024
Viewed by 432
Abstract
This study investigates the geomechanical behavior of five terrace orders in the Crotone Basin. The purpose is to understand the physical–mechanical parameters of these terraces to determine whether rock or soil mechanics principles should be applied for stability analysis. Samples were collected from [...] Read more.
This study investigates the geomechanical behavior of five terrace orders in the Crotone Basin. The purpose is to understand the physical–mechanical parameters of these terraces to determine whether rock or soil mechanics principles should be applied for stability analysis. Samples were collected from each terrace following an extensive field survey. Laboratory analyses were conducted to measure pulse velocities, uniaxial unconfined compressive strength, and compressive strength with truncated conical platens. The findings revealed key physical–mechanical parameters of the rocks, which are crucial for stability assessments. The Crotone Basin, known for its mineral resources such as hydrocarbons and rock salts, has been studied geologically since before the 1950s, but there is a lack of geomechanical data in existing literature. Therefore, the results presented here are novel and provide a basis for future studies on the instability of rocky slopes composed of similar soft rock types. These results will aid in accurate geological–geotechnical model reconstructions. While the findings can be applied to similar cases, it is important to note that each analysis site, despite showing similar phenomena, is unique and requires individual investigation. Full article
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<p>Geological sketch map of the study area and location of the samples taken from different terrace orders. Lithology symbol legend: 1. Slope deposits (Holocene); 2. Alluvial deposits (Holocene); 3. Beach deposits, conglomerates, and sands (Holocene); 4. Marly clays (Plio–Lower Pleistocene); 5. Dune sands (Holocene); 6. Sandstones, conglomerates, biocalcarenites (Terrace I, Middle Pleistocene); 7. Clastic deposits (Terrace II, Upper Pleistocene); 8. Bioturbed sandstones, biocalcarenites (Terrace III, Upper Pleistocene); 9. Calcarenites (Terrace IV, Upper Pleistocene); 10. Laminated and bioturbated sandstones (Terrace V, Upper Pleistocene).</p>
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<p>(<b>A</b>) Panoramic photo along the stretch of the ridge of Semaforo located in Via Olimpia, Crotone in which the flat areas of the terraces of the Sintema del Lago S.Anna are clearly visible (first-order terrace) and the badlands of the Cutro Clay formation. (<b>B</b>) Samples taken from the outcropping wall of the first-order terrace. (<b>C</b>) Detail of the limestone sample GC_5. (<b>D</b>) Detail of the arenaceous specimen CG_5.1. (<b>E</b>) Some cubic sandstone samples subjected to laboratory tests.</p>
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<p>(<b>A</b>) Limestone outcrop belonging to the second-order terrace. (<b>B</b>) Sample CG_3 detail where there is an arenaceous and a calcarenite portion. (<b>C</b>) Detail of the calcarenite portion of the cubic-shaped CG_3 sample. (<b>D</b>) Cubic sandstone and limestone samples subjected to laboratory tests.</p>
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<p>(<b>A</b>) Overview of the third-order terraces NW of Capo Colonna. (<b>B</b>–<b>F</b>) Calcarenite samples taken near Capo Colonna; (<b>C</b>,<b>E</b>,<b>G</b>) are the respective cubic-shaped specimens CG_6, 7, and 8. (<b>H</b>) Cubic-shaped specimens derived from samples of loc. Fratte.</p>
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<p>Area south of Isola Capo Rizzuto. (<b>A</b>) Fourth-order terrace southwest of the “Torre Vecchia” of Isola Capo Rizzuto. (<b>B</b>) Outcrop of the “Torre Vecchia” of Capo Rizzuto, showing evident slopes of landslides and materials deposited into the sea; note also the presence of clay tongues at the base (Cutro Clay). In areas with stable slopes, the measured calcarenite thicknesses reach approximately 10 m. (<b>C</b>) Altered calcarenite sample CG_4 with fossils. (<b>D</b>) CG_4 sample not exactly cubic in shape due to the low cementation rate and high porosity.</p>
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<p>Area south of Le Castella. (<b>A</b>) Stratified limestone with evidence of traces of bioturbations belonging to the fifth-order terrace. (<b>B</b>) Detail of the arenaceous specimen CG_1 with fossil elements; (<b>C</b>) Detail of the stratified arenaceous specimen CG_1.1; (<b>D</b>) Detail of the sample of calcarenite CG_2; (<b>E</b>–<b>G</b>) Cubic specimens of the respective samples (<b>B</b>–<b>D</b>).</p>
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<p>(<b>A</b>) Cubic specimens. (<b>B</b>) Oven at 40 °C. (<b>C</b>) Digital caliper BORLETTI CDJB15 150 mm. Equipment: (<b>D</b>) Matest ultrasonic pulse velocity tester; (<b>E</b>) Matest C070 for uniaxial compression test; (<b>F</b>) point load test.</p>
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<p>Graphs depicting the projected mean velocity values of ultrasonic waves (from <a href="#geosciences-14-00215-t002" class="html-table">Table 2</a>) for each sample from the different terrace orders. Specifically, Graph (<b>A</b>) corresponds to the first terrace order, Graph (<b>B</b>) to the second order, Graph (<b>C</b>) to the third order, Graph (<b>D</b>) to the fourth order, and Graph (<b>E</b>) to the fifth order. The colors in the graphs match those in the previous table to immediately identify and distinguish the different terrace orders. Specifically, there are five colors representing the five terrace orders, ranging from dark green (for the oldest terrace) to yellow (for the youngest terrace).</p>
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<p>Average velocities for the four series of analyzed samples. The colors in the graphs match those in the previous table to immediately identify and distinguish the different terrace orders. Specifically, there are five colors representing the five terrace orders, ranging from dark green (for the oldest terrace) to yellow (for the youngest terrace).</p>
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<p>Comparison between the mean strength values across the three axial directions for the five sample groups. The mean strength values along <span class="html-italic">y</span> and <span class="html-italic">x</span> for samples from the fourth and fifth terrace orders were not determined due to an insufficient number of samples.</p>
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<p>Comparison between the results obtained from the uniaxial compressive strength test and the point load test [<a href="#B33-geosciences-14-00215" class="html-bibr">33</a>,<a href="#B36-geosciences-14-00215" class="html-bibr">36</a>,<a href="#B37-geosciences-14-00215" class="html-bibr">37</a>,<a href="#B38-geosciences-14-00215" class="html-bibr">38</a>,<a href="#B39-geosciences-14-00215" class="html-bibr">39</a>,<a href="#B40-geosciences-14-00215" class="html-bibr">40</a>,<a href="#B41-geosciences-14-00215" class="html-bibr">41</a>,<a href="#B42-geosciences-14-00215" class="html-bibr">42</a>,<a href="#B43-geosciences-14-00215" class="html-bibr">43</a>,<a href="#B45-geosciences-14-00215" class="html-bibr">45</a>,<a href="#B46-geosciences-14-00215" class="html-bibr">46</a>].</p>
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<p>Correlation of uniaxial unconfined compressive strength and PLT results. The area under the orange dotted line represents the underestimation field of the UCS value by indirect methods, while the area above the line is the overestimation field.</p>
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45 pages, 18921 KiB  
Article
Reconstructing Impact of the 1867 Ionian Sea (Western Greece) Earthquake by Focusing on New Contemporary and Modern Sources for Building Damage, Environmental and Health Effects
by Spyridon Mavroulis, Maria Mavrouli, Efthymios Lekkas and Panayotis Carydis
Geosciences 2024, 14(8), 214; https://doi.org/10.3390/geosciences14080214 - 11 Aug 2024
Viewed by 661
Abstract
The 4 February 1867 Cephalonia (Western Greece) earthquake is the largest in the Ionian Islands and one of the largest in the Eastern Mediterranean. However, it remained one of the least studied historical events. For reconstructing this earthquake, we reevaluated existing knowledge and [...] Read more.
The 4 February 1867 Cephalonia (Western Greece) earthquake is the largest in the Ionian Islands and one of the largest in the Eastern Mediterranean. However, it remained one of the least studied historical events. For reconstructing this earthquake, we reevaluated existing knowledge and used new contemporary and modern sources, including scientific and local writers’ reports and books, local and national journals, newspapers, and ecclesiastical chronicles. The extracted information covered the earthquake parameters, population impact, building damage, and earthquake environmental effects (EEEs). The earthquake parameters included the origin time and duration of the main shock, epicenter location, precursors, aftershocks, and characteristics of the earthquake ground motion. The population impact involved direct and indirect health effects and population change. Building data highlighted the dominant building types and the types, grades, and distribution of damage. The EEEs included ground cracks, landslides, liquefaction, hydrological anomalies, and mild sea disturbances. Field surveys were also conducted for validation. The quantitative and qualitative information enabled the application of seismic intensity scales (EMS-98, ESI-07). The study concluded that since the affected areas were mainly composed of post-alpine deposits and secondarily of clay–clastic alpine formations with poor geotechnical properties, they were highly susceptible to failure. Effects and maximum intensities occurred in highly susceptible areas with a rich inventory. Full article
(This article belongs to the Section Natural Hazards)
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<p>The study area (yellow frame) comprising Lefkada (L), Cephalonia (C), Ithaki (I), and Zakynthos (Z) Islands is located east of the Cephalonia Transform Fault Zone (CTFZ) and a few km away from the Hellenic Trench (HT). K: Kerkyra; AA: Aetoloakarnania; CG: Central Greece; AC: Achaia; P: Peloponnese; TH: Thessaly, AT: Attica; CL: Cyclades Islands; CR: Crete Island. Active faults (red lines) are based on Styron and Pagani [<a href="#B10-geosciences-14-00214" class="html-bibr">10</a>]. Sources of the basemap: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community.</p>
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<p>Digitized microfilms of the publicly accessible collection of the Digital Library of the Hellenic Parliament [<a href="#B41-geosciences-14-00214" class="html-bibr">41</a>] presenting the front covers of the newspapers (<b>a</b>) “Anamorphosis” on 2/14 February 1867, (<b>b</b>) “Proinos Kirix” on 6/18 February 1867, (<b>c</b>) “Merimna” on 7/19 February 1867, and (<b>d</b>) “Ephimeris ton Filomathon” on 20 February/4 March 1867, referring to the 1867 Cephalonia earthquake disaster (dates of Julian calendar/Gregorian calendar).</p>
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<p>The first pages of the 4 February 1867 Cephalonia earthquake reports by (<b>a</b>) Schmidt [<a href="#B25-geosciences-14-00214" class="html-bibr">25</a>], (<b>b</b>) Vergotis [<a href="#B29-geosciences-14-00214" class="html-bibr">29</a>], (<b>c</b>) Solomos [<a href="#B31-geosciences-14-00214" class="html-bibr">31</a>], and Fouqué [<a href="#B28-geosciences-14-00214" class="html-bibr">28</a>].</p>
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<p>The flowchart of the applied methodology.</p>
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<p>Simplified neotectonic maps of Lefkada (<b>a</b>), Cephalonia and Ithaki (<b>b</b>), and Zakynthos (<b>c</b>) Islands based on Lekkas et al. [<a href="#B51-geosciences-14-00214" class="html-bibr">51</a>,<a href="#B52-geosciences-14-00214" class="html-bibr">52</a>] and Mavroulis et al. [<a href="#B2-geosciences-14-00214" class="html-bibr">2</a>], along with their fault blocks. Fault blocks of (i) Lefkada: Lefkada town (LT), Tsoukalades-Katouna (TK), Agios Nikitas (AN), Drymonas (DR), Mega Oros-Skaroi (MOS), Vlicho-Poros (VP), Sykeros-Achrada (SA), and Lefkata peninsula (LP). (ii) Cephalonia: Erissos Peninsula (EP), Paliki Peninsula (PP), the Aenos Mt and eastern Cephalonia (AMEC), Argostoli Peninsula (AP). (iii) Zakynthos: northern Zakynthos (NZ), western part of central Zakynthos (WCZ), eastern part of central Zakynthos (ECZ), Keri (KR), southern Zakynthos (SZ), Skopos Mt (SM). The earthquake epicenters are from the EPICA version 1.1 [<a href="#B23-geosciences-14-00214" class="html-bibr">23</a>].</p>
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<p>Simplified neotectonic maps of Lefkada (<b>a</b>), Cephalonia and Ithaki (<b>b</b>), and Zakynthos (<b>c</b>) Islands based on Lekkas et al. [<a href="#B51-geosciences-14-00214" class="html-bibr">51</a>,<a href="#B52-geosciences-14-00214" class="html-bibr">52</a>] and Mavroulis et al. [<a href="#B2-geosciences-14-00214" class="html-bibr">2</a>], along with their fault blocks. Fault blocks of (i) Lefkada: Lefkada town (LT), Tsoukalades-Katouna (TK), Agios Nikitas (AN), Drymonas (DR), Mega Oros-Skaroi (MOS), Vlicho-Poros (VP), Sykeros-Achrada (SA), and Lefkata peninsula (LP). (ii) Cephalonia: Erissos Peninsula (EP), Paliki Peninsula (PP), the Aenos Mt and eastern Cephalonia (AMEC), Argostoli Peninsula (AP). (iii) Zakynthos: northern Zakynthos (NZ), western part of central Zakynthos (WCZ), eastern part of central Zakynthos (ECZ), Keri (KR), southern Zakynthos (SZ), Skopos Mt (SM). The earthquake epicenters are from the EPICA version 1.1 [<a href="#B23-geosciences-14-00214" class="html-bibr">23</a>].</p>
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<p>The epicenter of the 4 February 1867 Cephalonia earthquake, along with the epicenters of significant earthquakes, based on the catalogue of Makropoulos et al. [<a href="#B11-geosciences-14-00214" class="html-bibr">11</a>]. Active faults come from Styron and Pagani [<a href="#B10-geosciences-14-00214" class="html-bibr">10</a>]. The CTFZ is located offshore, west of Lefkada and Cephalonia. Sources of the basemap: Esri, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community.</p>
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<p>Spatial distribution of the villages in Cephalonia that suffered casualties from the 4 February 1867 earthquake. PP: Paliki Peninsula, TV: Thinia Valley, EP: Erissos Peninsula, KM: Kalon Mt, ADM: Agia Dynati Mt, AEM: Aenos Mt, AP: Argostoli Peninsula.</p>
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<p>(<b>a</b>) The areas affected by the 4 February 1867 Cephalonia earthquake. (<b>b</b>) The villages and towns that suffered heavy to very heavy structural damage from the 1867 earthquake. They are distributed mainly in the western half of Cephalonia, and more specifically in the central and southern part of the Paliki Peninsula (PP), within the Thinia and Pylaros valleys (TV and PV, respectively), in a zone between the western ends of the Agia Dynati Mt (ADM) and the eastern coastal part of Argostoli Gulf (AG), in the Argostoli Peninsula (AP), south of the southern slopes of the Aenos Mt (AEM) and in Eastern Cephalonia, from Sami to Riza, along the western slopes of the Avgo and Atros Mts (AVM and ATM, respectively). Lighter damage was reported in the Erissos Peninsula (EP).</p>
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<p>The spatial distribution of Lefkada villages affected by the 4 February 1867 earthquake. They were mainly distributed in the south and southwestern part of the island.</p>
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<p>Typical views of structural damage caused by the 4 February 1867 earthquake to houses with load-bearing masonry walls in the town of Lixouri, located in the Paliki Peninsula. The photographs were first published by Fouqué [<a href="#B28-geosciences-14-00214" class="html-bibr">28</a>], in his report on the 1867 earthquake. Partial collapse of the masonry is visible (damage of grades 4 and 5 according to the EMS-98 guidelines).</p>
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<p>The EMS-98 intensity map of the 4 February 1867 Cephalonia earthquake.</p>
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<p>The type and the spatial distribution of the secondary environmental effects triggered by the 4 February 1867 earthquake in Cephalonia were mainly observed or reported in the Paliki Peninsula, with the Argostoli and Erissos peninsulas following with very few affected localities.</p>
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<p>The environmental seismic intensities for the 4 February 1867 earthquake. The maximum intensity VIII–IX<sub>ESI-07</sub> was assigned to the Paliki Peninsula (PP), with the intensity VIII<sub>ESI-07</sub> assigned to both Paliki and the neighboring Argostoli Peninsula (AP). In the Thinia valley (TV) and the Erissos Peninsula (EP), the lowest intensity VI–VII<sub>ESI-07</sub> was assigned. In the Agia Dynati Mt (ADM) and the Aenos Mt (AEM), no intensities were assigned due to the absence of triggered effects.</p>
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<p>Map showing the spatial distribution of residential areas (red dots) in Cephalonia affected by the 4 February 1867 earthquake, in relation to the geological structure. It is clear that the affected towns and villages are located exclusively in areas composed of post-alpine deposits and clay–clastic alpine formations (details of the geological formations are provided in the neotectonic map shown in <a href="#geosciences-14-00214-f005" class="html-fig">Figure 5</a>b), with the properties that make them susceptible to failure. Almost all settlements founded on alpine formations of the Ionian and Paxoi geological units remained intact after the earthquake. PV: Pylaros Valley, TV: Thinia valley, AG: Argostoli Gulf, ADM: Agia Dynati Mt, AEM: Aenos Mt.</p>
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<p>Map showing the spatial distribution of the residential areas in Lefkada affected by the 4 February 1867 earthquake, in relation to the geological structure. It is clear that the majority of the settlements affected in the southern and southwestern part of the island are located in areas structured by post-alpine and clay–clastic alpine deposits (details of the geological formations in <a href="#geosciences-14-00214-f005" class="html-fig">Figure 5</a>a), with characteristics that make them prone to failure and earthquake-induced building damage. The affected villages in southwestern Lefkada are located within a small part (Dragano—Athani Graben, DAG) of the fault block of the Lefkada Peninsula (LP). The observed damage is attributed to the active tectonics and the highly disrupted formations within and along the margins of the DAG.</p>
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<p>Comparison of the 4 February 1867 earthquake-triggered landslides and liquefaction inventory, with the relevant susceptibility maps of Cephalonia presented by Mavroulis et al. [<a href="#B5-geosciences-14-00214" class="html-bibr">5</a>] and Mavroulis and Lekkas [<a href="#B4-geosciences-14-00214" class="html-bibr">4</a>].</p>
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22 pages, 7039 KiB  
Article
Mineralogical and Engineering Properties of Soils Derived from In Situ Weathering of Tuff in Central Java, Indonesia
by I Gde Budi Indrawan, Daniel Tamado, Mifthahul Abrar and I Wayan Warmada
Geosciences 2024, 14(8), 213; https://doi.org/10.3390/geosciences14080213 - 10 Aug 2024
Viewed by 650
Abstract
This paper presents the results of borehole investigations and laboratory tests carried out to characterize the soils derived from in situ weathering of tuff in Central Java, Indonesia. The 70 m thick weathering profile of the Quaternary tuff consisted of residual soil and [...] Read more.
This paper presents the results of borehole investigations and laboratory tests carried out to characterize the soils derived from in situ weathering of tuff in Central Java, Indonesia. The 70 m thick weathering profile of the Quaternary tuff consisted of residual soil and completely to highly decomposed rocks. The relatively low dry unit weight and cohesion but high water content, porosity, plastic and liquid limits, and angle of internal friction of the soils in the present study were related to the dominance of halloysite clay minerals. The established relationships to predict soil shear strength parameters from the soil plasticity index and standard penetration test (SPT) N-values were examined, and linear and non-linear relationships for soils derived from in situ weathering of tuff were proposed. Full article
(This article belongs to the Special Issue Soil-Structure Interactions in Underground Construction)
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<p>Location of the study area according to PT JJB [<a href="#B22-geosciences-14-00213" class="html-bibr">22</a>]. The inset map and digital elevation model are from the Geospatial Information Agency of Indonesia [<a href="#B23-geosciences-14-00213" class="html-bibr">23</a>].</p>
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<p>Borehole locations and depths from PT JJB [<a href="#B22-geosciences-14-00213" class="html-bibr">22</a>] are plotted on the engineering geological map and cross section developed in the present study. The material weathering grade in each borehole estimated from the present study is drawn. The base map is from the Geospatial Information Agency of Indonesia [<a href="#B23-geosciences-14-00213" class="html-bibr">23</a>].</p>
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<p>Typical outcrop of brown residual soil and yellowish brown completely and highly decomposed tuffs. RS: residual soil; CD: completely decomposed tuff; HD: highly decomposed tuff.</p>
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<p>Typical core samples. (<b>a</b>,<b>b</b>) Residual soils at 2–3 m and 9–10 m depths; (<b>c</b>,<b>d</b>) completely decomposed tuffs at 14–15 m and 24–25 m depths; (<b>e,f</b>) Highly decomposed tuff at 69–70 m depth.</p>
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<p>Photomicrographs of core samples classified as crystal tuffs in parallel polarized light (<b>left</b>) and crossed polarized light (<b>right</b>) [<a href="#B1-geosciences-14-00213" class="html-bibr">1</a>]. (<b>a</b>) Completely decomposed tuff (17–18 m depth); (<b>b</b>) highly decomposed tuff (31–32 m depth); (<b>c</b>) highly decomposed tuff (67–68 m depth).</p>
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<p>(<b>a</b>) Profile of clay mineral content identified from petrographic analyses; (<b>b</b>) profile of common minerals identified from XRD analyses (data points from PT JJB [<a href="#B22-geosciences-14-00213" class="html-bibr">22</a>]); (<b>c</b>) profile of soil classification based on the USCS (data points are partly from Tamado [<a href="#B2-geosciences-14-00213" class="html-bibr">2</a>]).</p>
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<p>(<b>a</b>) Profile of water content; (<b>b</b>) profile of bulk unit weight; (<b>c</b>) profile of dry unit weight; (<b>d</b>) profile of porosity; (<b>e</b>) profile of saturation degree; (<b>f</b>) profile of specific gravity (data points are partly from Tamado [<a href="#B2-geosciences-14-00213" class="html-bibr">2</a>]). Dashed line indicates general trend.</p>
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<p>(<b>a</b>) Profile of plastic limit; (<b>b</b>) profile of liquid limit; (<b>c</b>) profile of plasticity index; (<b>d</b>) profile of liquidity index (data points are partly from Tamado [<a href="#B2-geosciences-14-00213" class="html-bibr">2</a>]). Dashed line indicates general trend.</p>
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<p>(<b>a</b>) Profile of soil penetration resistance (data points from [<a href="#B12-geosciences-14-00213" class="html-bibr">12</a>]); (<b>b</b>,<b>c</b>) profiles of soil cohesion and angle of internal friction (data points are partly from Tamado [<a href="#B2-geosciences-14-00213" class="html-bibr">2</a>]). Dashed line indicates general trend.</p>
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<p>(<b>a</b>) Relationship between ratio of cohesion to SPT N-values and plasticity index; (<b>b</b>) relationship between cohesion and plasticity index.</p>
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<p>Relationships between angle of internal friction and plasticity index.</p>
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<p>Data points in <a href="#geosciences-14-00213-f009" class="html-fig">Figure 9</a> are plotted to establish the N<sub>60</sub>-c<sub>u</sub> relationship. The published N-S<sub>u</sub> and N<sub>60</sub>-S<sub>u</sub> relationships for fine-grained, clay, and residual soils are plotted for comparison.</p>
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<p>N<sub>60</sub>-c<sub>u</sub> relationships for the soils from 0 to 30 m depth and from 30 to 70 m depth.</p>
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<p>Relationships between penetration resistance and angle of internal friction and resistance to movement.</p>
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12 pages, 3253 KiB  
Article
Neural Network-Based Climate Prediction for the 21st Century Using the Finnish Multi-Millennial Tree-Ring Chronology
by Elena A. Kasatkina, Oleg I. Shumilov and Mauri Timonen
Geosciences 2024, 14(8), 212; https://doi.org/10.3390/geosciences14080212 - 8 Aug 2024
Viewed by 550
Abstract
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records [...] Read more.
The sun’s activity role in climate change has become a topic of debate. According to data from the IPCC, the global average temperature has shown an increasing trend since 1850, with an average increase of 0.06 °C/decade. Our analysis of summer temperature records from five weather stations in northern Fennoscandia (65°–70.4° N) revealed an increasing trend, with a range of 0.09 °C/decade to 0.15 °C/decade. However, due to the short duration of instrumental records, it is not possible to accurately assess and predict climate changes on centennial and millennial timescales. In this study, we used the Finnish super-long (~7600 years) tree-ring chronology to create a climate prediction for the 21st century. We applied a method that combines a long short-term memory (LSTM) neural network with the continuous wavelet transform and wavelet filtering in order to make climate change predictions. This approach revealed a significant decrease in tree-ring growth over the near term (2063–2073). The predicted decrease in tree-ring growth (and regional temperature) is thought to be a result of a new grand solar minimum, which may lead to Little Ice Age-like climatic conditions. This result is significant for understanding current climate processes and assessing potential environmental and socio-economic risks on a global and regional level, including in the area of the Arctic shipping routes. Full article
(This article belongs to the Special Issue Advanced Statistical Modelling in Climate Change)
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<p>Map showing sample collection sites with subfossil pines [<a href="#B32-geosciences-14-00212" class="html-bibr">32</a>] (triangles) and weather stations (black circles): 1—Vardo, 2—Teriberka, 3—Murmansk, 4—Sodankyla, 5—Kem. The blue dashed line indicates the Arctic circle.</p>
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<p>Mean summer (JJA) temperatures across northern Fennoscandia: (<b>a</b>) Murmansk, (<b>b</b>) Teriberka, (<b>c</b>) Kem, (<b>d</b>) Sodankyla, (<b>e</b>) Vardo. Red lines denote trends calculated using the nonparametric Kendall–Theil robust line regression method [<a href="#B50-geosciences-14-00212" class="html-bibr">50</a>]. The numbers indicate the increasing rate (°C/decade), with the 95% confidence interval in square brackets.</p>
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<p>A block diagram of the developed LSTM network for climate change prediction.</p>
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<p>(<b>a</b>) Finnish super-long tree-ring chronology (FLTR) [<a href="#B32-geosciences-14-00212" class="html-bibr">32</a>], (<b>b</b>) corresponding continuous wavelet transform (CWT), and (<b>c</b>) wavelet-filtered chronology over the 300–400-year band (blue) with predicted values using the LSTM (red). The 95% confidence level against red noise is shown as a black contour.</p>
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<p>Comparison of the measured (blue) and predicted by the LSTM (red) time series of the FLTR over the testing period (1398–2003 A.D.) (<b>a</b>) and the difference between predicted and measured FLTR values (<b>b</b>).</p>
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28 pages, 15379 KiB  
Article
Vegetation, Climate and Habitability in the Marseille Basin (SE France) circa 1 Ma
by Valérie Andrieu, Pierre Rochette, François Fournier, François Demory, Mary Robles, Odile Peyron, Séverine Fauquette, Eliane Charrat, Pierre Magniez, Belinda Gambin and Samuel Benoît De Coignac
Geosciences 2024, 14(8), 211; https://doi.org/10.3390/geosciences14080211 - 7 Aug 2024
Viewed by 655
Abstract
The environment of the Marseille basin in the Early Pleistocene was reconstructed through a multiproxy study of fluvial tufa deposits. Palaeomagnetic measurements revealed the Jaramillo subchron and dated the tufa to within the 0.8–1.5 Ma interval, probably between 0.9 and1.2 Ma. Sedimentological studies [...] Read more.
The environment of the Marseille basin in the Early Pleistocene was reconstructed through a multiproxy study of fluvial tufa deposits. Palaeomagnetic measurements revealed the Jaramillo subchron and dated the tufa to within the 0.8–1.5 Ma interval, probably between 0.9 and1.2 Ma. Sedimentological studies show varied depositional environments comprising natural dams formed by accumulations of plants promoting the development of upstream water bodies. The very negative δ13C values indicate that the Marseille tufa is not travertine sensu stricto but tufa deposited by local cold-water rivers. Palynological analyses indicate a semi-forested, diverse, mosaic vegetation landscape dominated by a Mediterranean pine and oak forest. Along the streams, the riparian forest was diverse and included Juglans, Castanea, Platanus and Vitis. The potential diet reconstructed from pollen was varied. The most surprising discovery was the presence of proto-cereals, which could potentially enrich the diet with carbohydrates. The identification of spores of coprophilous fungi seems to indicate the presence in situ of large herbivore herds. It is possible that, as in Anatolia, the disturbance of ecosystems by large herbivores was responsible for the genetic mutation of Poaceae and the appearance of proto-cereals. Climatic reconstructions indicate a slightly cooler and wetter climate than the present. Full article
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<p>Simplified geological map of the Marseille basin and location of the samples.</p>
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<p>(<b>A</b>) Tufa cutting with a water-cooled circular saw; (<b>B</b>) sample of Saint Exupéry High School.</p>
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<p>Sedimentary logs of various sections in the Saint Exupéry High School.</p>
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<p>Sedimentary logs of La Calade section.</p>
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<p>Sedimentary logs of La Viste section.</p>
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<p>(<b>A</b>–<b>F</b>): Demagnetisation analyses for 6 representative samples. Full/open squares are upper/lower hemisphere for the equal-area plots’ horizontal/vertical component on the orthogonal plots. Points used for calculation of ChRM directions are in red.</p>
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<p>Reversal angle versus depth of the Plan d’Aou section, with picture of the section. Solid diamonds correspond to directions determined using full demagnetisation data, while grey diamonds correspond to a blanket demagnetisation at 150 °C.</p>
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<p>Pictures of tufa outcrops in the Marseille basin. (<b>A</b>) Saint-Exupéry High School: phytohermal tufa (<b>Lst</b>) with coated vertical stems of reeds (cf. <span class="html-italic">Phragmites</span>), interbedded with calcarenitic tufas (<b>Sb</b>). (<b>B</b>) Saint-Exupéry High School: high-relief barrage formed by the in situ calcite coating of accumulated plant fragments (Phytoclastic rudstone: <b>Lph</b>). This barrage is onlapped by cm- to dm-thick layers of calcarenitic tufas (<b>Sb</b>), some of them displaying a wavy bedding (<b><span class="html-italic">w.</span></b>) resulting from soft sediment deformation processes. A block of phytoclastic rudstone (<b><span class="html-italic">fb</span></b>.), likely fallen from the steep barrage wall, is encased within <b>Sb</b> tufas. Karstic cavities (<b>k</b>.) are common within phytoclastic tufas. (<b>C</b>) La Calade section: oncolitic rudstones (<b>Lo</b>) infilling channels (<b>ch.</b>), incising calcarenitic tufas (<b>Sb</b>).</p>
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<p>Conceptual depositional model for the lower Pleistocene continental sedimentation in the Marseille basin (adapted from [<a href="#B28-geosciences-14-00211" class="html-bibr">28</a>]). <b>Lph</b>: phytoclastic rudstone (barrage); <b>Lst</b>: Phytohermal tufa (paludal environments with reeds); <b>Sb</b>: bioclastic-peloidal calcarenites (low-to-medium energy dammed environments); <b>Lo</b>: oncoidal rudstones (channel fills); <b>cg.</b>: conglomerates (braided channel fills or bars); <b>sl</b>.: silts (floodplain); <b>fb.</b>: fallen blocks.</p>
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<p>δ<sup>13</sup>C vs. δ<sup>18</sup>O cross-plot of bulk carbonates from calcareous tufa from the Marseille basin (see <a href="#geosciences-14-00211-f001" class="html-fig">Figure 1</a> for location of sampling localities).</p>
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<p>(<b>A</b>) Cerealia L = 50.16 μm; (<b>B</b>) Cerealia L = 46.02 μm; (<b>C</b>) Cerealia L = 43.66 μm; (<b>D</b>) Cerealia L = 43.26 μm; (<b>E</b>) <span class="html-italic">Secale</span> sp. L = 61.15 μm; (<b>F</b>) <span class="html-italic">Delitschia</span> L = 20.3 μm; (<b>G</b>) <span class="html-italic">Coniochaeta</span> L = 14.63 μm; (<b>H</b>) <span class="html-italic">Valsaria</span> sp. L = 24.59 μm; (<b>I</b>) <span class="html-italic">Olea</span> sp. L = 22.39 μm; (<b>J</b>) <span class="html-italic">Vitis</span> sp. L = 23.75 μm; (<b>K</b>) <span class="html-italic">Castanea</span> sp. L = 18.63 μm; (<b>L</b>) Cichorioideae L = 33.07 μm; (<b>M</b>) Rumex sp. L = 33.8 μm; (<b>N</b>) Plantago lanceolata sp. L = 32.3 μm; (<b>O</b>) Poacaeae L = 31.62 μm; (<b>P</b>) Poacaeae L = 37.52 μm.</p>
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<p>(<b>A</b>) <span class="html-italic">Picea</span> sp. L = 149.73 μm; (<b>B</b>) <span class="html-italic">Pinus sylvestris</span> sp. L = 61.15 μm; (<b>C</b>) <span class="html-italic">Abies</span> sp. L = 125.10 μm; (<b>D</b>) Mediterranean <span class="html-italic">Pinus</span> L = 98.37 μm; (<b>E</b>) <span class="html-italic">Cedrus</span> sp. L = 96.25 μm; (<b>F</b>) <span class="html-italic">Betula</span> sp. L = 22.72 μm; (<b>G</b>) Deciduous <span class="html-italic">Quercus</span> L = 32.63 μm; (<b>H</b>) <span class="html-italic">Ostrya</span>/<span class="html-italic">Carpinus orientalis</span> L = 27.36 μm; (<b>I</b>) <span class="html-italic">Artemisia</span> sp. L = 19.66 μm; (<b>J</b>) Chenopodiaceae L = 26.57 μm; (<b>K</b>) <span class="html-italic">Juniperus</span> sp. L = 50.74 μm; (<b>L</b>) <span class="html-italic">Charcoal</span> sp. L of the longest = 85.03 μm.</p>
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<p>Synthetic pollen diagram of the Marseille tufa.</p>
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<p>Synthetic pollen diagram of the Marseille tufa: trees 1.</p>
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<p>Synthetic pollen diagram of the Marseille tufa: trees 2.</p>
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<p>Pollen-inferred climate reconstructions of tufa samples from Marseille are based on five methods: MAT (modern analogue technique), WA-PLS (weighted averaging partial least squares regression), RF (random forest), BRT (boosted regression trees) and CAM (climatic amplitude method). Six climatic parameters have been reconstructed: MAAT (mean annual air temperature), MTWA (mean temperature of the warmest month), MTCO (mean temperature of the coldest month), PANN (mean annual precipitation), Pwinter (mean winter precipitation) and Psummer (mean summer precipitation). The error bars indicate the root mean square error (RMSE). Dashed lines correspond to modern climate values of Marseille’s samples obtained from WorldClim 2 [<a href="#B59-geosciences-14-00211" class="html-bibr">59</a>].</p>
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<p>The potential plant diet reconstructed from pollen data from the Marseille tufa (<b>A</b>,<b>B</b>) and Acıgöl (<b>C</b>,<b>D</b>), Turkey (redrawn [<a href="#B5-geosciences-14-00211" class="html-bibr">5</a>]).</p>
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17 pages, 7084 KiB  
Article
Asbestos Hazard in Serpentinite Rocks: Influence of Mineralogical and Structural Characteristics on Fiber Potential Release
by Lorenzo Marzini, Marco Iannini, Giovanna Giorgetti, Filippo Bonciani, Paolo Conti, Riccardo Salvini and Cecilia Viti
Geosciences 2024, 14(8), 210; https://doi.org/10.3390/geosciences14080210 - 5 Aug 2024
Viewed by 606
Abstract
Naturally occurring asbestos (NOA) represents a matter of social and environmental concern due to its potential release in the atmosphere during rock excavation and grinding in quarry and road tunnel activities. In most cases, NOA occurs in serpentinites, i.e., rocks deriving from low-grade [...] Read more.
Naturally occurring asbestos (NOA) represents a matter of social and environmental concern due to its potential release in the atmosphere during rock excavation and grinding in quarry and road tunnel activities. In most cases, NOA occurs in serpentinites, i.e., rocks deriving from low-grade metamorphic hydration of mantle peridotites. The potential release of asbestos fibers from serpentinite outcrops depends on several features, such as serpentinization degree, rock deformation, weathering, and abundance of fibrous veins. In this study, we selected a set of serpentinite samples from a representative outcrop in Tuscany (Italy), and we analyzed them by Optical, Scanning, and Transmission Electron Microscopies. The samples were treated by grinding tests following the Italian guidelines Decrees 14/5/96 and 152/2006 for the determination of the Release Index (RI), i.e., the fiber amount released through controlled crushing tests. The fine-grained powder released during the tests was analyzed by quantitative Fourier transform infrared spectroscopy (FTIR) to determine the variety and the amount of released fibers and to assess the potential hazard of the different serpentinite samples. Results indicate that the amount of released fibers is mostly related to serpentinite deformation, with the highest RI values for cataclastic and foliated samples, typically characterized by widespread occurrence of fibrous veins. Conversely, massive pseudomorphic serpentinite revealed a very low RI, even if their actual chrysotile content is up to 20–25%. Based on our original findings from the RI results, a preliminary investigation of the outcrop at the mesoscale would be of primary importance to obtain a reliable hazard assessment of NOA sites, allowing the primary distinction among the different serpentinites lithotypes and the effective fiber release. Full article
(This article belongs to the Section Natural Hazards)
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<p>Serpentinite outcrops, sample location (coordinate system: Gauss-Boaga, west zone) and geological map (adapted from Db Geologico Regionale [<a href="#B55-geosciences-14-00210" class="html-bibr">55</a>]).</p>
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<p>Calabrian massive serpentinite samples location (image from Google Earth).</p>
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<p>Main lithotypes in serpentinitic outcrops: massive, undeformed serpentinites (MS) with mesh and bastite pseudomorphic textures; foliated serpentinites (FS), formed by pressure solution and subsequent dynamic precipitation of new serpentines in fibrous/pseudofibrous veins; cataclastic serpentinites (CS), dominated by brittle fracturing and grain size reduction.</p>
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<p>Grinding test mechanical apparatus scheme: (A) steel cylinder; (B) rotating rollers; (C) tilting of the cylinder through the hand screw; (D) security grid; (E) faucet with valve.</p>
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<p>Crossed nicols, polarizing microscope images of massive serpentinites: (<b>a</b>) partially hydrated peridotite, with lizardite rims (gray) and preserved olivine cores (yellow); (<b>b</b>) mesh texture in pseudomorphic serpentinites, consisting of lizardite rims and polyphasic serpentine cores; (<b>c</b>) hourglass pseudomorphic texture, mainly formed by lizardite “sectors”; the orange arrow points to a bastitic lamella, formed by pyroxene serpentinization.</p>
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<p>Crossed nicols, polarizing optical microscope images of foliated serpentinites: (<b>a</b>,<b>b</b>) deformed mesh texture, with preferential pressure solution of cores and development of lizardite “ribbon” textures; (<b>c</b>) typical sigmoidal chrysotile veinlets, cutting previous pseudomorphic textures; (<b>d</b>) crack-and-seal serpentine vein with a common association of polygonal serpentine and chrysotile.</p>
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<p>SEM images (both backscattered, BSE, and secondary electrons, SE) of different pale green veins and shear zones, surrounding massive dark green serpentinites: (<b>a</b>) Chrysotile isoriented fibers from a monomineralic sigmoidal vein, up to 500 µm thick; (<b>b</b>) Chrysotile long fibers from a larger shear zone; (<b>c</b>) Antigorite lamellae from a splintery pale green vein; (<b>d</b>) Tremolite fibers in irregular veins and patches within cataclastic serpentinites.</p>
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<p>Crossed nicols, polarizing microscope images of cataclastic serpentinites: (<b>a</b>) clasts of serpentine pseudomorphs (meshes and bastites), embedded within a fine-to-ultrafine serpentine matrix; (<b>b</b>,<b>c</b>) details of mesh and bastite clasts, respectively.</p>
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<p>Boxplots of FTIR quantitative data showing Release Index in the three main data acquisition lithotypes (“MS”—Massive Serpentinites, “CS”—Cataclasite, and “FS”—Serpentine veins). The box represents data within the first and the third quartiles; the dot symbol represents the mean value; the line within the box represents the median value.</p>
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<p>Boxplots of FTIR data showing Release Index in the two different study areas considered (“MS Rognosi Mounts” and “MS Calabria”). The box represents data within the first and the third quartiles; the dot symbol represents the mean value; the line extending parallel from the box is the whisker in the range 10–90; the line within the box represents the median value.</p>
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33 pages, 18761 KiB  
Article
Earthquake Precursors: The Physics, Identification, and Application
by Sergey Pulinets and Victor Manuel Velasco Herrera
Geosciences 2024, 14(8), 209; https://doi.org/10.3390/geosciences14080209 - 5 Aug 2024
Viewed by 870
Abstract
The paper presents the author’s vision of the problem of earthquake hazards from the physical point of view. The first part is concerned with the processes of precursor’s generation. These processes are a part of the complex system of the lithosphere–atmosphere–ionosphere–magnetosphere coupling, which [...] Read more.
The paper presents the author’s vision of the problem of earthquake hazards from the physical point of view. The first part is concerned with the processes of precursor’s generation. These processes are a part of the complex system of the lithosphere–atmosphere–ionosphere–magnetosphere coupling, which is characteristic of many other natural phenomena, where air ionization, atmospheric thermodynamic instability, and the Global Electric Circuit are involved in the processes of the geosphere’s interaction. The second part of the paper is concentrated on the reliable precursor’s identification. The specific features helping to identify precursors are separated into two groups: the absolute signatures such as the precursor’s locality or equatorial anomaly crests generation in conditions of absence of natural east-directed electric field and the conditional signatures due to the physical uniqueness mechanism of their generation, or necessity of the presence of additional precursors as multiple consequences of air ionization demonstrating the precursor’s synergy. The last part of the paper is devoted to the possible practical applications of the described precursors for purposes of the short-term earthquake forecast. A change in the paradigm of the earthquake forecast is proposed. The problem should be placed into the same category as weather forecasting or space weather forecasting. Full article
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<p>Vertical profiles of ionization of the atmosphere by solar electromagnetic radiation, energetic particle fluxes, and galactic cosmic rays [<a href="#B29-geosciences-14-00209" class="html-bibr">29</a>].</p>
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<p>Vertical profiles of air ionization from the ground surface up to 10 km altitude. Blue hatching—galactic cosmic rays; red hatching—ground radioactivity (modified from [<a href="#B30-geosciences-14-00209" class="html-bibr">30</a>]).</p>
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<p>Simplified model of the Global Electric Circuit.</p>
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<p>(<b>a</b>) Deviation of critical frequency <span class="html-italic">foF2</span> from 15-days median (color coded) registered by Kiev ionosonde in coordinates Days (horizontal axis)—UTC (vertical axis); (<b>b</b>) Deviation of critical frequency <span class="html-italic">foF2</span> obtained by epoch overlay method for 9 earthquakes of M ≥ 5.4 in Italy at the distance less than 300 km from Rome by the data of the Rome ionosonde.</p>
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<p>(<b>a</b>) Differential TEC map on 27 January 2011; (<b>b</b>) Differential TEC map on 8 March 2011, 3 days before the M9 Tohoku earthquake. Volcano position and earthquake epicenter are shown by a white cross.</p>
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<p>Schematic presentation of the near-ground kinetics with the basic ion’s formation.</p>
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<p>Variation in concentration of heavy ions and air conductivity during a week [<a href="#B47-geosciences-14-00209" class="html-bibr">47</a>].</p>
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<p>(<b>a</b>) Percental deviation of GPS TEC at Petropavlovsk-Kamchatsky (color coded) versus local time (vertical axis), DOY (horizontal axis); (<b>b</b>) Time series of the DPS TEC percental deviation, a red triangle indicates the moment of the M7.5 earthquake at northern Kurils; (<b>c</b>) Time series of specific resistivity measured by geoacoustic emission in the G-1 deep well on frequency 160 Hz at Petropavlovsk-Kamchatsky, a red arrow indicates the moment of the M7.5 earthquake at northern Kurils.</p>
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<p>Schematic diagram of atmosphere ionization by radons (after [<a href="#B51-geosciences-14-00209" class="html-bibr">51</a>]).</p>
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<p>(<b>a</b>) Negative variation in atmospheric electric field 21 h before the M5 earthquake at Kamchatka peninsula on the 24th September 1997 (vertical black arrow indicates the moment of the earthquake [<a href="#B51-geosciences-14-00209" class="html-bibr">51</a>]); (<b>b</b>) Variation in the vertical electric field around the time of the 20 kT nuclear test in the atmosphere in 1952 [<a href="#B52-geosciences-14-00209" class="html-bibr">52</a>]. Zero indicates the moment of the explosion. Bold line indicates the electric field variations, dashed line shows the trend of natural value of atmospheric electric field recovering.</p>
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<p>Variations in the semi-transparence coefficient of the sporadic <span class="html-italic">E</span>-layer Δf<sub>b</sub>E<sub>s</sub> around the time of the Tohoku M9 earthquake on 11 March 2011 and after explosions at Fukushima NPP. The maximal peaks of Δ<span class="html-italic">f<sub>b</sub>E<sub>s</sub></span> are marked by burgundy arrows.</p>
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<p>(<b>a</b>) Formation of elevated <span class="html-italic">E<sub>s</sub></span> at 120 km altitude (orange oval) in comparison with (<b>b</b>) <span class="html-italic">E<sub>s</sub></span> normal position at 100 km altitude [<a href="#B49-geosciences-14-00209" class="html-bibr">49</a>].</p>
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<p>From <b>top</b> to <b>bottom</b>: air temperature, relative humidity, and air pressure over the epicenter of the M7.3 Fukushima earthquake. Coordinates of the epicenter: Lat 38.23° N and Lon 141.77° E. Shadowed rectangle is the precursory period; burgundy line marks the moment of the earthquake.</p>
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<p>(<b>a</b>) Relative humidity map; (<b>b</b>) Air pressure map.</p>
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<p>(<b>a</b>) Average of near- and intermediate-field of ACP (corrected for pressure changes—blue) and shear-traction field (red) in the epicentral area of the 16 March 2022, Fukushima, Japan, earthquake (time shown with grey vertical line). The ACP follows the temporal evolution of the shear-traction field before the earthquake, while the spike in ACP happens close to the increase in shear-traction; (<b>b</b>) Long-term correlation between shear-traction field assimilative model in rose and atmospheric chemical potential (ACP) in blue, close to the Andreanof Islands, Alaska, USA.</p>
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<p>Map of ACP spatial distribution within the zone of Fukushima M7.3 earthquake preparation acquired on 11 March 2022, 5 days before the earthquake.</p>
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<p>From <b>top</b> to <b>bottom</b>: air temperature, relative humidity, and ACP over Fukushima NPP in March 2011. Vertical lines indicate the period of explosions.</p>
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<p>(<b>a</b>) Spatial distribution of Cs<sup>137</sup> on 6 May 1986 at 12:00 UTC; (<b>b</b>) Spatial distribution of ACP on 6 May 1986 at 12:00 UTC. Asterisks show the position of Chernobyl NPP and arrows and ovals show similarities in distributions.</p>
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<p>Schematic presentation of the LAIC model.</p>
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<p>(<b>a</b>) Determination of the size of the earthquake preparation zone in relation to magnitude according to [<a href="#B71-geosciences-14-00209" class="html-bibr">71</a>]. (<b>b</b>) Radon and geochemical precursors of earthquake distribution versus magnitude according to [<a href="#B74-geosciences-14-00209" class="html-bibr">74</a>]. The single bold line characterizes the empirical relationship of [<a href="#B75-geosciences-14-00209" class="html-bibr">75</a>] who calibrated the maximum distance of a radon anomaly for a given magnitude on the basis of a shear dislocation of an earthquake. Dashed line <span class="html-italic">L</span> characterizes the typical rupture length of active faults as a function of magnitude by using the empirical law of [<a href="#B76-geosciences-14-00209" class="html-bibr">76</a>]. Modified from [<a href="#B45-geosciences-14-00209" class="html-bibr">45</a>].</p>
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<p>(<b>a</b>) Left panel—UT GIM map for 1000 UT on 9 May 2009, 3 days before the Wenchuan M7.9 earthquake in China; <b>right</b> panel—combined LT map for 1700 LT corresponding to 1000 UT in China; (<b>b</b>) <b>Left</b> panel—15-day median GIM map for 1000 UT; right panel—combined LT map for 1700 LT corresponding to 1000 UT in China; (<b>c</b>) Left panel and right panel—differences between the (<b>a</b>,<b>b</b>) corresponding maps; (<b>d</b>) Left panel and right panel—increased images outlined by red rectangles in the corresponding images (<b>c</b>). Blue circle—earthquake preparation zone. At all panels red star indicates the position of the Wenchuan earthquake epicenter.</p>
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<p>(<b>a</b>) Differential GIM TEC map registered 4 days before the Kamchatka M6.9 earthquake on 3 March 2023. Epicenter position is marked by a yellow spot, software detected epicenter position is marked by blue star; (<b>b</b>) ACP spatial distribution for Kamchatka M7.2 earthquake registered a few weeks before the main shock on 30 January 2016, Dobrovolsky zone is marked by black oval; (<b>c</b>) The series of OLR thermal spots positions (small circles) registered before the Nepal M7.8 earthquakes in 2015; black stars indicate positions of the Nepal M7.8 earthquakes on 25 April and 17 May 2015 [<a href="#B83-geosciences-14-00209" class="html-bibr">83</a>], Dobrovolsky zone is marked by red circle; (<b>d</b>) Distribution of electron concentration large deviations along the DEMETER satellite orbits before the Chile M8.8 earthquake on 27 February 2010 and the Chile M6.3 earthquake on 19 November 2007; red circles indicate the ionospheric anomalies detected by DEMETER satellite while passing over the earthquake preparation zone, blue triangles indicate the position of epicenter determined automatically by the data processing software, yellow stars indicate the real epicenter position [<a href="#B84-geosciences-14-00209" class="html-bibr">84</a>].</p>
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<p>(<b>a</b>) Statistical distribution of the number of registered ionospheric signals as a function of time in relation to the day of earthquake and distance of satellite orbit from the epicenter for the DEMETER satellite (5426 earthquakes M &gt; 5) [<a href="#B86-geosciences-14-00209" class="html-bibr">86</a>]; (<b>b</b>) Statistical distribution of the number of registered ionospheric signals as a function of time in relation to the day of earthquake and distance of satellite orbit from the epicenter for the CSES 1 satellite [<a href="#B87-geosciences-14-00209" class="html-bibr">87</a>].</p>
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<p>Schematic diagram of the machine processing of ionosphere monitoring and space weather data for the purpose of cognitive identification of earthquake precursors.</p>
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16 pages, 14559 KiB  
Article
Heavy Minerals Distribution and Provenance in Modern Beach and Fluvial Sands of the Betic Cordillera, Southern Spain
by Anna Chiara Tangari, Daniele Cirillo, Raffaella De Luca, Domenico Miriello, Elena Pugliese and Emilia Le Pera
Geosciences 2024, 14(8), 208; https://doi.org/10.3390/geosciences14080208 - 5 Aug 2024
Viewed by 954
Abstract
This study uses heavy detrital minerals to determine actualistic fluvial and beach sand provenance across the Betic Cordillera (Spain), along the coast from Almeria to Marbella. The Betic Cordillera, primarily composed of metamorphic rocks to the east, supply an assemblage dominated by almandine [...] Read more.
This study uses heavy detrital minerals to determine actualistic fluvial and beach sand provenance across the Betic Cordillera (Spain), along the coast from Almeria to Marbella. The Betic Cordillera, primarily composed of metamorphic rocks to the east, supply an assemblage dominated by almandine and graphite, with a longshore dispersal from Almeria to Malaga. Buergerite and hypersthene indicate the provenance of calcalkaline lavas east of Cabo de Gata. The western part of the Betic Cordillera, which comprises the Ronda Peridotite Complex, supplies a chromite and diopside assemblage, with a dispersal from Marbella to Algeciras. Considering these mineralogical suites, the effects of source rock compositions and weathering are evaluated. The heavy mineral species mirror the mineralogy of the source rocks of local outcrops and wider source terranes. The fluvial heavy mineral suites do not differ significantly from those in the beaches except for some unstable species. Unstable species such as olivine, pyroxene, and amphibole do not show evidence of loss because of elevated topography and semiarid climate, which do not affect heavy minerals. This contribution also evaluates the potential of some heavy detrital species as ideal pathfinders in searching for diamonds. Full article
(This article belongs to the Special Issue Tectonic Evolution and Paleogeography of Plate Boundaries)
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<p>(<b>a</b>) Location map of the Iberian Peninsula and northwestern part of Africa; (<b>b</b>) geological map of the Iberian and northwestern sector of Africa modified after [<a href="#B12-geosciences-14-00208" class="html-bibr">12</a>]; (<b>c</b>) tectono-stratigraphic map of southwestern Spain (Betic Cordillera) and northwestern sector of Morocco. The legend of the geological map of panel (<b>a</b>) is shown in <a href="#app1-geosciences-14-00208" class="html-app">Supplementary Figure S1</a> [<a href="#B12-geosciences-14-00208" class="html-bibr">12</a>].</p>
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<p>Location map of the sample sites and name of the localities. Triangle: river samples; circle: beach samples.</p>
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<p>Raman spectra of pyroxene in the Almeria–Malaga petrofacies. (<b>a</b>) Hypersthene (Hyp) and (<b>b</b>) diopside (Di) in Las Negras beach; (<b>c</b>) Augite (Aug) and (<b>d</b>) omphacite (Omp) occurring in Rio Guadalmansa.</p>
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<p>Raman spectra of amphibole in the Almeria–Malaga petrofacies. (<b>a</b>) Hornblende (Hbl) and (<b>b</b>) kaersutite (Krs) detected in Las Negras beach; (<b>c</b>) tremolite (Tr) occurring in the Lagos beach; (<b>d</b>) cummingtonite (Omp) identified in Las Negras beach.</p>
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<p>Raman spectra of heavy minerals detected in Almeria–Malaga petrofacies. (<b>a</b>) Garnets such as almandine (Alm) identified in Rio Adra, (<b>b</b>) staurolite (St) detected in Lagos beach; (<b>c</b>) chloritoid (Cld) occurring in Rio Morales; (<b>d</b>) sillimanite (Sil) identified in Punta de la Mona beach; (<b>e</b>) epidote (Ep) identified in Rio Adra; (<b>f</b>) tourmaline detected as buergerite (Bur) in Las Negras beach.</p>
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<p>Raman spectra of heavy minerals in Almeria–Malaga and Marbella petrofacies, respectively. (<b>a</b>) Monazite (Mnz) in Punta del la Mona beach; (<b>b</b>) actinolite (Act) detected in Punta del Castor; (<b>c</b>) forsterite-type (Fo) olivine and (<b>d</b>) chromite (Chr) in Rio Guadalmina.</p>
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<p>Raman spectrum of graphite in Punta de la Mona beach, in the Almeria–Malaga petrofacies.</p>
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<p>Distribution of heavy mineral percentages in Almeria–Malaga and Marbella petrofacies with the location of the sampling sites. Tur = tourmaline (Bur); Mnz = monazite; Amp = amphibole (Hbl, Cum, Tr, Act); Cpx = clinopyroxene (Di, Aug, Omp); Opx = Orthopyroxene (Hyp); Fo = forsterite; Spl = Spinel; Ep = Epidote; Grt = Garnet (Alm); MM = metasedimentary heavy minerals (St, Cld, Sil).</p>
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<p>(<b>a</b>) Geological map of the southwestern part of the Betic Cordillera (from [<a href="#B64-geosciences-14-00208" class="html-bibr">64</a>]), with the location sites (black rectangles and square); (<b>b</b>) 2D map view of the lithology of Marbella study area with the location sites; (<b>c</b>) 2D map view of the lithology of Malaga study area with the location sites; (<b>d</b>) 2D map view of the lithology of Almeria site with the location sites. The legend of this map is shown in the <a href="#app1-geosciences-14-00208" class="html-app">Supplementary Figure S2</a> [<a href="#B70-geosciences-14-00208" class="html-bibr">70</a>].</p>
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22 pages, 9040 KiB  
Article
Thermally Induced Moisture Flow in a Silty Sand under a 1-D Thermal Gradient
by Nice Kaneza, Aashish Pokhrel, Laureano R. Hoyos and Xinbao Yu
Geosciences 2024, 14(8), 207; https://doi.org/10.3390/geosciences14080207 - 2 Aug 2024
Viewed by 526
Abstract
Thermally induced moisture flow in unsaturated soils involves complex coupled thermal–hydro processes with the moisture flow in both the vapor and liquid phases. The accurate measurement of the moisture flow in unsaturated sands remains a challenging task due to low moisture migration, the [...] Read more.
Thermally induced moisture flow in unsaturated soils involves complex coupled thermal–hydro processes with the moisture flow in both the vapor and liquid phases. The accurate measurement of the moisture flow in unsaturated sands remains a challenging task due to low moisture migration, the temperature effect on moisture sensors, and the gravity effect on moisture flow. This study aims to accurately measure transient moisture flow, heat transfer, and thermal conductivity in a silty sand with 35% non-plastic fines in a closed heat cell with a controlled 1-D temperature gradient. The heat cell consists of two temperature-controlled heat exchanger plates, heat flux sensors, moisture sensors, thermocouples, and thermal conductivity sensors. The soil moisture sensors were calibrated in the test soil at room temperature and then at elevated incremental temperatures. Soil samples compacted at various initial moisture contents were tested under a constant 1-D temperature gradient of 4 °C/cm. Soil moisture redistribution, temperature, and thermal conductivity profiles were determined from the test results. Transient temperature responses indicated that a lower initial moisture content led to a higher temperature drop after reaching the peak, or a more concaved temperature profile in a steady state due to enhanced moisture migration driven by the temperature gradients. Dry soils exhibited uniform thermal properties, while moist soils showed varying thermal conductivity profiles. A critical moisture content was identified when the maximum moisture migration occurred. Thermal conductivity in soils increased with the distance from the heat source due to thermally induced moisture migration. These findings provide valuable insights into coupled moisture–heat flow dynamics in unsaturated sands. Full article
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<p>Schematics of the soil heating cell setup.</p>
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<p>Photos of the heating cell during soil compaction placement.</p>
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<p>Heat exchanger plate with spiral channels (<b>Left</b>) and water circulator (<b>Right</b>).</p>
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<p>Soil particle size distribution curves of the silty sand.</p>
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<p>Soil compaction curve.</p>
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<p>Soil water retention characteristics of the silty sand.</p>
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<p>Setup for thermal conductivity (<b>Left</b>) and EC-5 VWC (<b>Right</b>) measurement at room temperature.</p>
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<p>VWC readings for EC-5 sensor.</p>
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<p>Temperature correction for EC-5 sensor for VWC = 0.077 m<sup>3</sup>/m<sup>3</sup>, dry density.</p>
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<p>Temperature correction for VWC 0.055, 0.147, and 0.206.</p>
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<p>Comparison of the two thermal conductivity sensors.</p>
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<p>Thermal conductivity comparison of KS-1 and SH-3 sensors.</p>
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<p>Temperature variations with time for (<b>a</b>) VWC = 0.000, (<b>b</b>) VWC = 0.055, (<b>c</b>) VWC = 0.078, (<b>d</b>) VWC = 0.172, and (<b>e</b>) VWC = 0.249 m<sup>3</sup>/m<sup>3</sup>.</p>
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<p>Temperature profile during different stages of the heating test for (<b>a</b>) 0.000, (<b>b</b>) 0.055, (<b>c</b>) 0.078, (<b>d</b>) 0.172, and (<b>e</b>) 0.249 m<sup>3</sup>/m<sup>3</sup>.</p>
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<p>Raw EC-5 VWC data for initial VWC of 0.078 m<sup>3</sup>/m<sup>3</sup>.</p>
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<p>Soil moisture variation with time for initial VWC of (<b>a</b>) 0.055, (<b>b</b>) 0.078, (<b>c</b>) 0.172, and (<b>d</b>) 0.249 m<sup>3</sup>/m<sup>3.</sup></p>
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<p>Initial and steady-state moisture profile from oven drying for initial moisture content of (<b>a</b>) 0.055, (<b>b</b>) 0.078, (<b>c</b>) 0.172, and (<b>d</b>) 0.249 m<sup>3</sup>/m<sup>3</sup>.</p>
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<p>Moisture change at (<b>a</b>) 10.0 cm and (<b>b</b>) 0.0 cm.</p>
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<p>Schematics for the thermal conductivity analyses.</p>
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<p>Thermal conductivity at 0.055 m<sup>3</sup>/m<sup>3</sup> moisture content during heating test.</p>
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<p>(<b>a</b>) Thermal conductivity profile at steady state and (<b>b</b>) equivalent thermal conductivity and heat flux of the heating cell at steady state.</p>
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20 pages, 4575 KiB  
Article
Zircons from Eclogite-Associated Rocks of the Marun–Keu Complex, the Polar Urals: Trace Elements and U–Pb Dating
by Laysan Salimgaraeva, Aleksey Berezin, Sergey Sergeev, Nikolai Gubanov, Ekaterina Stetskaya and Sergey Skublov
Geosciences 2024, 14(8), 206; https://doi.org/10.3390/geosciences14080206 - 2 Aug 2024
Viewed by 508
Abstract
The Marun–Keu complex plays a significant role in our understanding of the geological evolution of the Ural orogen; however, it remains poorly understood. This study aims to provide insights into the complex’s age, protolith composition, rock formation conditions, and its position in the [...] Read more.
The Marun–Keu complex plays a significant role in our understanding of the geological evolution of the Ural orogen; however, it remains poorly understood. This study aims to provide insights into the complex’s age, protolith composition, rock formation conditions, and its position in the geological history. The zircons from the host granitic gneiss are characterized by magmatic cores with an age of 473 Ma and metamorphic rims with an age of approximately 370 Ma. We suggest that the metamorphic rims were formed during eclogite metamorphism and that the metagranitoids hosting the eclogites experienced eclogite metamorphism simultaneously with the basic and ultrabasic rocks that are common in this area. Heterogeneous zircons were also isolated from the selvage of a pegmatite vein, in which four domains are distinguished, two to three of which can be identified within single grains, as follows: (1) igneous cores with an age of approximately 470 Ma and the geochemical characteristics of zircon crystallized in basic rocks; (2) zircons recrystallized during eclogite metamorphism with geochemical characteristics intermediate between those of the magmatic cores and true eclogitic zircon; (3) pegmatitic zircon, exhibiting the most sharply differentiated REE spectra of all four domains, characterized by a prominent positive Ce anomaly and a weakly expressed negative Eu anomaly; and (4) eclogitic zircon, observed in the form of veins and rims, superimposed in relation to the other three domains. The age of the latter three domains is within the error range and is estimated to be approximately 370 Ma. This indicates that the processes of eclogite metamorphism and the formation of pegmatites occurred at approximately the same time in the studied area. Full article
(This article belongs to the Section Geochemistry)
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<p>(<b>a</b>) Simplified tectonic scheme of the Urals after [<a href="#B3-geosciences-14-00206" class="html-bibr">3</a>]. MUF—Main Uralian Fault. 1—the Cis-Ural foredeep; 2—the Central Ural megazone; 3—the East Ural megazone; 4—the West Ural megazone; 5—the Tagil–Magnitogorsk megazone; 6—the Trans-Ural megazone. (<b>b</b>) The inset shows the overall geographic location of the Marun–Keu complex. (<b>c</b>) Geological map of the Marun–Keu complex [<a href="#B5-geosciences-14-00206" class="html-bibr">5</a>]. Key: 1—Quaternary; 2—Ordovician (?); 3—greenschists of the Nyarovey formation; 4—Marun–Keu series: gneisses, eclogites; 5—gneisses, granite gneisses; 6—granites with fluorite; 7—meta-rhyolites; 8—diorites, 9—gabbroids; 10—ultramafic rocks (Syum–Keu complex); 11—predominately eclogites; 12—glaucophane-hosted rocks; 13—quartz–graphite schists; 14—faults.</p>
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<p>(<b>a</b>) Schematic sampling map. (<b>b</b>) General view of the peak 1040 area. (<b>c</b>) Selvage of a pegmatite vein at the contact with its host peridotites; white circle with sample number indicates the location from which sample 2209 was collected. (<b>d</b>) Contact between granitic gneisses and peridotites, cross-cut by a 1.5 m-wide pegmatite vein; white circle with sample number indicates the location from which sample 2218 was collected. The pegmatite vein was previously excavated for exploration purposes. (<b>e</b>) Migmatization zone in granitic gneisses indicated by a blue dashed line.</p>
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<p>Microphotographs of thin section of granitic gneiss (sample 2218): (<b>a</b>,<b>c</b>) transmitted polarized light; (<b>b</b>,<b>d</b>) birefringence.</p>
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<p>(<b>a</b>) CL images of zircons from granitic gneiss (sample 2218). Colored circles indicate the SIMS analytical spots, the numbers of which correspond to those in <a href="#app1-geosciences-14-00206" class="html-app">Table S2</a> and <a href="#geosciences-14-00206-f006" class="html-fig">Figure 6</a>. Blue circles indicate magmatic cores, while green circles indicate metamorphic rims. (<b>b</b>) BSE images of zircons from granitic gneiss (sample 2218) with labeled mineral inclusions. <span class="html-italic">Png</span>—<span class="html-italic">phengite</span>.</p>
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<p>(<b>a</b>) CL images of zircons from the selvage of a pegmatite vein (sample 2209). The colored circles indicate the SIMS analytical spots, the numbers of which correspond to those in <a href="#app1-geosciences-14-00206" class="html-app">Table S3</a> and <a href="#geosciences-14-00206-f007" class="html-fig">Figure 7</a>. Blue circles indicate magmatic cores (first domain), green circles indicate recrystallized cores (second domain), navy blue circles indicate pegmatite zircon (third domain), and yellow circles indicate eclogite rims and veinlets (fourth domain). (<b>b</b>,<b>c</b>) BSE images of zircons from the selvage of a pegmatite vein (sample 2209) with labeled mineral inclusions. <span class="html-italic">Png</span>—<span class="html-italic">phengite</span>.</p>
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<p>Trace element compositions of zircons from granitic gneiss (sample 2218). (<b>a</b>) REE distribution patterns normalized to CI chondrite [<a href="#B33-geosciences-14-00206" class="html-bibr">33</a>]. (<b>b</b>–<b>d</b>) Co-variation diagrams of element pairs. The position of the analytical spots in different zircon domains is shown in <a href="#geosciences-14-00206-f004" class="html-fig">Figure 4</a> by circles of the corresponding colors.</p>
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<p>Trace element compositions of zircons from the selvage of a pegmatite vein (sample 2209). (<b>a</b>) REE distribution patterns normalized to CI chondrite [<a href="#B33-geosciences-14-00206" class="html-bibr">33</a>]. (<b>b</b>–<b>d</b>) Co-variation diagrams of element pairs. The position of the analytical spots in different zircon domains is shown in <a href="#geosciences-14-00206-f005" class="html-fig">Figure 5</a> by circles of the corresponding colors.</p>
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<p>Geochemical discriminant diagrams for zircon after [<a href="#B35-geosciences-14-00206" class="html-bibr">35</a>]. Blue markers indicate the positions of zircon cores from the selvage of the pegmatite vein (sample 2209).</p>
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<p>Concordia plot for zircons (<b>a</b>) from granitic gneiss (sample 2218) and (<b>b</b>) from the selvage of a pegmatite vein (sample 2209). Error ellipses are at 2σ confidence. The blue ellipses represent the concordia age and error. Decay constant errors are included.</p>
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18 pages, 5242 KiB  
Article
The Contributions of Tectonics, Hydrochemistry and Stable Isotopes to the Water Resource Management of a Thermal–Mineral Aquifer: The Case Study of Kyllini, Northwest Peloponnese
by Vasiliki Stavropoulou, Anastasia Pyrgaki, Eleni Zagana, Christos Pouliaris and Nerantzis Kazakis
Geosciences 2024, 14(8), 205; https://doi.org/10.3390/geosciences14080205 - 2 Aug 2024
Viewed by 1289
Abstract
This study aims to investigate the intricate relationship between geological structures, water chemistry, and isotopic composition in order to gain a deeper understanding of the origins and recharge mechanisms of thermal–mineral waters in the Kyllini region. The research integrates tectonic analysis, hydrochemical data, [...] Read more.
This study aims to investigate the intricate relationship between geological structures, water chemistry, and isotopic composition in order to gain a deeper understanding of the origins and recharge mechanisms of thermal–mineral waters in the Kyllini region. The research integrates tectonic analysis, hydrochemical data, and stable isotope measurements to delineate recharge zones and trace the origin of these unique water sources. The methods used for delineation are the geological and tectonic study of the area, as well as hydrochemical and isotopic data analysis. The findings highlight that tectonic activity creates preferential flow paths and consequently influences the hydrogeological framework, facilitating deep circulation and the upwelling of thermal waters. Monthly analyses of groundwater samples from the Kyllini thermal spring were conducted over one hydrological year (2019–2020) and compared with data from the area collected in 2009. The hydrochemical profiles of major and minor ions reveal distinct signatures corresponding to various water–rock interactions, while stable isotope analysis provides insights into the climatic conditions and altitudes of recharge areas. Hydrochemical analyses reveal the composition of thermal–mineral waters, aiding in the identification of potential sources and their evolution. The conceptualization of Kyllini contributes to the deeper understanding of the intricate interplay between tectonics, hydrochemistry, and stable isotopes. During a hydrological year, the water type of Kyllini’s spring groundwater remains the same (Na-Cl-HCO3), presenting only slight alterations. Full article
(This article belongs to the Section Hydrogeology)
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<p>Geological map of the broader area.</p>
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<p>Geological cross section of the broader area (modified by EAGME).</p>
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<p>(<b>a</b>) Piper diagram and scatter plots of (<b>b</b>) Cl-Na, (<b>c</b>) Cl-Ca, (<b>d</b>) Cl-SO<sub>4</sub>, and (<b>e</b>) Cl-Mg/Ca. The black lines represent the concentration–dilution line of seawater.</p>
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<p>(<b>a</b>) Shoeller diagram of samples. Fresh waters are depicted with blue and green color lines and thermo-mineral waters with red, pink, and maroon color lines and scatter plots; (<b>b</b>) B-Cl; (<b>c</b>) T(wa)-Li (T(wa) is temperature of water).</p>
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<p>Radial diagram showing spatial trends in water composition (major anions and cations) of Kyllini Spring water in (<b>a</b>) 2009 and (<b>b</b>) 2020.</p>
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<p>(<b>a</b>) Diagram of stable isotopes d<sup>18</sup>O (‰) and dD (‰) with Global Meteoric Water Line and Eastern Mediterranean Meteoric Water Line and (<b>b</b>) diagram of altitude (m) and d<sup>18</sup>O (‰) of water samples.</p>
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<p>Frequency of saturation indices of (<b>a</b>) quartz, (<b>b</b>) calcite, (<b>c</b>) dolomite, and (<b>d</b>) barite.</p>
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29 pages, 19572 KiB  
Article
Morphology, Internal Architecture, Facies Model, and Emplacement Mechanisms of Lava Flows from the Central Atlantic Magmatic Province (CAMP) of the Hartford and Deerfield Basins (USA)
by Abdelhak Moumou, Nasrrddine Youbi, Hind El Hachimi, Khalil El Kadiri, José Madeira, João Mata, Isma Amri and Abdelkarim Ait Baha
Geosciences 2024, 14(8), 204; https://doi.org/10.3390/geosciences14080204 - 31 Jul 2024
Viewed by 450
Abstract
The morphology, internal architecture, and emplacement mechanisms of the Central Atlantic Magmatic Province (CAMP) lava flows of the Hartford and Deerfield basins (USA) are presented. The Talcott, Holyoke, and Hampden formations within the Hartford basin constitute distinct basaltic units, each exhibiting chemical, mineralogical, [...] Read more.
The morphology, internal architecture, and emplacement mechanisms of the Central Atlantic Magmatic Province (CAMP) lava flows of the Hartford and Deerfield basins (USA) are presented. The Talcott, Holyoke, and Hampden formations within the Hartford basin constitute distinct basaltic units, each exhibiting chemical, mineralogical, and structural differences corresponding to flow fields. Each flow field was the result of several sustained eruptions that produced both inflated pahoehoe flows and subaquatic extrusions: 1–5 eruptions in the Talcott formation and 1–2 in Holyoke and Hampden basalts, where simple flows are dominant. The Deerfield basin displays the Deerfield basalt unit, characterized by pillow lavas and sheet lobes, aligning chemically and mineralogically with the Holyoke basalt unit. Overall, the studied flow fields are composed of thick, simple pahoehoe flows that display the entire range of pahoehoe morphology, including inflated lobes. The three-partite structure of sheet lobes, vertical distribution of vesicles, and segregation structures are typical. The characteristics of the volcanic pile suggest slow emplacement during sustained eruptive episodes and are compatible with a continental basaltic succession facies model. The studied CAMP basalts of the eastern United States are correlated with the well-exposed examples on both sides of the Atlantic Ocean (Canada, Portugal, and Morocco). Full article
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<p>(<b>a</b>) location map of Africa–South America–North America, Greenland, and Europe at 201 Ma and CAMP schematic extent; (<b>b</b>) paleogeographic extent of ca 201 Ma Central Atlantic Magmatic Province (CAMP) across the central Pangean supercontinent (after McHone [<a href="#B6-geosciences-14-00204" class="html-bibr">6</a>,<a href="#B23-geosciences-14-00204" class="html-bibr">23</a>,<a href="#B28-geosciences-14-00204" class="html-bibr">28</a>]).</p>
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<p>(<b>a</b>) Early Mesozoic rift basins in eastern North America: 1, Fundy; 2, Hartford; 3, Newark; 4, Gettysburg; 5, Culpeper; 6, Danville; (<b>b</b>) Geologic sketch map of Hartford, Deerfield, and Pomperaug (Southbury) basins; (<b>c</b>) Stratigraphic column of Newark Supergroup in the Hartford basin (after [<a href="#B23-geosciences-14-00204" class="html-bibr">23</a>]).</p>
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<p>Ideal diagram showing feature structures of inflated pahoehoe sheet lobes [<a href="#B70-geosciences-14-00204" class="html-bibr">70</a>]. The left side of the column shows the characteristic three-part division of sheet lobes (<b>a</b>) and jointing styles (<b>b</b>) CRZ, crustal zone; PLZ, platy zone; CLZ, columnar zone. The right side of the column illustrates the distribution of (<b>c</b>) vesiculation structures (VZ, vesicular zone; MV, mega-vesicle; HVS, horizontal vesicle sheet; VC, vesicle cylinder; SV, segregation vesicle; PV, pipe vesicle; BVZ, basal vesicular zone), (<b>d</b>) vesiculation (non- to sparsely vesicular d = 0–5 vol%, moderately vesicular m = 10–20 vol% and vesicular v = 30–40 vol%), and (<b>e</b>) degree of crystallinity (G, hyaline; hyh, hypohyaline; hc, hypocrystalline; c, holocrystalline). The scale h/l indicates the normalized height above the base of the sheet lobe (h, height in lobe; l, total lobe thickness).</p>
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<p>Studied sections of the Talcott Basalt Formation from the Hartford Basin, Connecticut (modified from [<a href="#B34-geosciences-14-00204" class="html-bibr">34</a>,<a href="#B93-geosciences-14-00204" class="html-bibr">93</a>]). 1. Section Behind the Target store in Meriden section (N 41°33′9.34″; W 72°49′0.40″); 2. Tariffville section (N 41°54′28.73″; W 72°45′40.18″); 3. King Philip’s Cave section, Talcott Mountain State Park (N 41°50′1.99″; W 72°47′54.08″). BC: Basal Crust; LC: Lava Core; ULC: Upper Lava Crust.</p>
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<p>The large, long rock-cut behind the Target store in Meriden, Connecticut (<b>a</b>) Contact between the New Haven Formation and the first pillowed flow of the Talcott Basalt Formation. Legend: 1, Blak mudstone, contorted with flame structure and isolated pillows; 2, Contact; 3, Closely (densely) packed pillow. (<b>b</b>) Contact between the first pillowed flow and the second sheet lobe flow of the Talcott Basalt Formation. Note the occurrence of pipe vesicles in the basal crust of the second sheet lobe and the preservation of the glassy zone and radial and concentric cracks. (<b>c</b>) View of the flow units 3, 4, and 5 of the Talcott Basalt Formation. The flow unit 3 is composed of pillow breccias of 2 m gradually overcome by a horizon of 1 m with well-preserved isolated pillow and fragment pillow dispersed in an abundant hyaloclastite matrix which is covered by less than one meter of vesicular lava. The flow unit 4 is constituted by densely packed pillows. Note its compound nature. The last flow unit 5 is a vesicular lava flow. (<b>d</b>) View of the flows units 3, 4, and 5 of the Talcott Basalt Formation. Note the compound nature of the flow units 3 and 4. The flow unit 4 with densely packed pillows is emplaced in paleochannel.</p>
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<p>Plot of pillows’ horizontal (H) vs. vertical (V) dimensions from (<b>a</b>) unit 1 of Talcott Basalt from (Behind the Target outcrop (Hartford Basin); (<b>b</b>) unit 1 of the Deerfield basalt from the Springfield outcrop (Deerfield Basin).</p>
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<p>Pipe vesicles and large pillow lava located in the large, long rock cut behind the Target store in Meriden, Connecticut (<b>a</b>) Internal structure of pillow lava of the first pillowed flow of the Talcott Basalt Formation (toward the top of the unit). Location: The large, long rock-cut behind the Target store in Meriden, Connecticut. (<b>b</b>) Detail of the contact between the first pillowed flow and the second sheet lobe flow of the Talcott Basalt Formation. Note the occurrence of unfilled pipe vesicles in the basal crust of the second sheet lobe.</p>
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<p>Pictures took in the large, long rock-cut behind the Target store in Meriden, Connecticut. (<b>a</b>,<b>b</b>) Closely (densely) packed pillow; the first pillowed flow of the Talcott Basalt Formation (toward the top of the unit). Note that the hyaloclastite matrix between pillows becomes more abundant. (<b>c</b>) Contact between the first pillowed flow and the second sheet lobe flow of the Talcott Basalt Formation. Note the occurrence of pipe vesicles in the basal crust of the second sheet lobe and the preservation of the glassy zone and radial and concentric cracks. (<b>c</b>) Mega-vesicles, including half-moon vesicles and horizontal vesicle sheet of the second sheet lobe flow of the Talcott Basalt Formation. Segregation structures of pahoehoe flow types are located at the top of the lava core of the love. Location: The large, long rock-cut behind the Target store in Meriden, Connecticut. (<b>d</b>) Large pillow lava with digitation form of the first pillowed flow of the Talcott Basalt Formation (toward the top of the unit).</p>
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<p>(<b>a</b>) Fluidal peperites of the Neptunian dyke of <a href="#geosciences-14-00204-f009" class="html-fig">Figure 9</a>b Photograph of jigsaw-fit clasts in basaltic breccia from the Neptunian dyke. This type of jigsaw-fit texture is a common feature of blocky and fluidal peperites and is thought to reflect in situ quench fragmentation. (<b>b</b>) Blockey peperite of the Neptunian dyke of <a href="#geosciences-14-00204-f009" class="html-fig">Figure 9</a>b arkose host sediments inject fissures of blocky juvenile clast with typical jigsaw-fit texture, indicating that the peperite has typical features of blocky peperite. Blocky peperite is formed in the background of magma, producing brittle crackings. When hot magma intrudes cold wet sediments, hot magma generates quenching distortion and forms juvenile clasts. (<b>c</b>) Neptunian dyke (clastic dyke) cross-cutting the Talcott Basalt Formation at about 3 km ESE of the quarry of Tilcon Connecticut/North Branford on Fox Road. This basalt breccia-filled fissure with an arkosic matrix and peperites (both blocky and fluidal peperites are present) shows a chilled margin and injected Arkose pocket with the Talcott Basalt. Blocky peperites and mixed blocky and fluidal peperites formed where rising melt interacted explosively with groundwater and with coarse, water-saturated sediments of the New Haven Formation, and underwent brittle quench fragmentation. See <a href="#app1-geosciences-14-00204" class="html-app">Figures S13 and S14</a> for details of peperites. (<b>d</b>) Fluidal peperites in the Contact of the Hampden Basalt with the East Berlin Fm near Berlin along the RT-9 South. (<b>e</b>) Detail of the well-developed layers of blackish spherule layers (accretionary lapilli?). (<b>f</b>) Well-developed layers of blackish spherule layers (accretionary lapilli?) that might represent basaltic lapilli occur a few cm below the first pillowed flow of the Talcott Basalt Formation located in the large, long rock-cut behind the Target store in Meriden, Connecticut.</p>
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<p>Panoramic view of the Holyoke flood-basalt flow in the North Branford Quarry, Connecticut. Note the Cuspate boundary (dashed line) separating radiating joints in entablature from vertical columnar joints in the colonnade of the Holyoke flood-basalt flow. The boundary is approximately 120 m above the base of this 200-m-thick section through the flow. The centers of the two cusps are 15 m apart.</p>
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<p>Lithostratigraphic columns across the Holyoke basalts in the Hartford and Deerfield basins (modified from [<a href="#B34-geosciences-14-00204" class="html-bibr">34</a>,<a href="#B93-geosciences-14-00204" class="html-bibr">93</a>]). 1. Section of the Tilcon quarry near North Branford Town, Connecticut (N 41°20′31.81″; W 72°47′39.28″); 2. section of Cooks Gap, Plainville, Hartford County, Connecticut (N 41°40′28.72″; W 72°49′44.95″); 3. and 4. sections of Deerfield basalt sequence at French King Highway, Gill (3) (N 41°40′28.72″; W 72°49′44.95″) and Turner Falls; (4) (N 41°40′28.72″; W 72°49′44.95″), Massachusetts. Note that each lithostratigraphic column has its scale. BC: basal crust. LCR: lava core. ULC: upper lava crust.</p>
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<p>(<b>a</b>) The two flows of the Holyoke Basalt in the Tom Holyoke Mountains Quarry, Massachusetts. (<b>b</b>,<b>c</b>) Vesicle Cylinder of the lava core of the Holyoke flood-basalt flow in the North Branford Quarry, Connecticut. (<b>d</b>) Fault/Squeez up N30–40, 75–85 SW affecting the first flow of the Holyoke Basalt in the Tom Holyoke Mountains Quarry, Massachusetts. (<b>e</b>) Segregation sheets of coarse-grained ferrodiorite of the Holyoke flood-basalt flow in the North Branford Quarry, Connecticut.</p>
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<p>Lithostratigraphic columns across the Hampden basalts in the Hartford basin (modified from Gray [<a href="#B34-geosciences-14-00204" class="html-bibr">34</a>]). 1. Section along the RT-9 South, Berlin, Connecticut (N 41°37′19.05″; W 72°44′11.07″); 2. Section of the Rock Ridge Park, Hartford, Connecticut (N 41°45′3.08″; W 72°41′36.67″).</p>
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<p>(<b>a</b>,<b>b</b>) Horizontal vesicular zone of Hampden Basalt at the Section of the Rock Ridge Park, Hartford, Connecticut; (<b>c</b>) contact of the Hampden Basalt with the East Berlin Fm. near Berlin along the RT-9 South; (<b>d</b>) contact of the Hampden Basalt with the East Berlin Fm. near Berlin along the RT-9 South with pipe vesicles; (<b>e</b>) ash bed (Pompton Tuff Bed) of the East Berlin Fm. with orange color near Berlin along the RT-9 South, Connecticut; (<b>g</b>) flow top breccia of Hampden Basalt at the Section of the Rock Ridge Park, Hartford, Connecticut; (<b>f</b>) detail of Flow top breccia of Hampden Basalt at the Section of the Rock Ridge Park, Hartford, Connecticut.</p>
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<p>(<b>a</b>) Contact between the Deerfield Basalt and the Sugarloaf Arkose Formation near Greenfield along the Mohawk Trail (RT-2A). Left side before crossing the French King Bridge and entering Greenfield. The development of peperites underlines the contact. (<b>b</b>) Horizontal vesicular zone of the Upper Crust of the second sheet lobe of the Deerfield Basalt Formation near Greenfield along the Mohawk Trail (RT-2A). Right side before reaching the French King Bridge and entering Greenfield.</p>
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13 pages, 10183 KiB  
Article
Tectonic Inversion and Deformation Differences in the Transition from Ionian Basin to Apulian Platform: The Example from Ionian Islands, Greece
by Avraam Zelilidis, Nicolina Bourli, Elena Zoumpouli and Angelos G. Maravelis
Geosciences 2024, 14(8), 203; https://doi.org/10.3390/geosciences14080203 - 31 Jul 2024
Viewed by 465
Abstract
The studied areas (the Ionian Islands: Paxoi, Lefkas, Kefalonia, and Zakynthos), are situated at the western ends of the Ionian Basin in contact with the Apulian Platform and named as Apulian Platform Margins. The proposed model is based on fieldwork, previously published data, [...] Read more.
The studied areas (the Ionian Islands: Paxoi, Lefkas, Kefalonia, and Zakynthos), are situated at the western ends of the Ionian Basin in contact with the Apulian Platform and named as Apulian Platform Margins. The proposed model is based on fieldwork, previously published data, and balanced geologic cross-sections. Late Jurassic to Early Eocene NNW–SSE extension, followed by Middle Eocene to Middle Miocene (NNW–SSE compression, characterizes the Ionian basin). The space availability, the distance of the Ionian Thrust from the Kefalonia transform fault and the altitude between the Apulian Platform and the Ionian Basin that was produced during the extensional regime were the main factors for the produced structures due to inversion tectonics. In Zakynthos Island, the space availability (far from the Kefalonia Transform Fault), and the reactivation of normal bounding faults formed an open geometry anticline (Vrachionas anticline) and a foreland basin (Kalamaki thrust foreland basin). In Kefalonia Island, the space from the Kefalonia Transform Fault was limited, and the tectonic inversion formed anticline geometries (Aenos Mountain), nappes (within the Aenos Mountain) and small foreland basins (Argostoli gulf), all within the margins. In Lefkas Island, the lack of space, very close to the Kefalonia Transform Fault, led to the movement of the Ionian Basin over the margins, attempting to overthrust the Apulian Platform. Because the obstacle between the basin and the platform was very large, the moving part of the Ionian Basin strongly deformed producing nappes and anticlines in the external part of the Ionian Basin, and a very narrow foreland basin (Ionian Thrust foreland basin). Full article
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<p>Geological map of the studied area with the four studied cross-sections [<a href="#B28-geosciences-14-00203" class="html-bibr">28</a>].</p>
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<p>(<b>a</b>) Geological map and (<b>b</b>) geomorphological map of Paxoi and Anti-Paxoi Island.</p>
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<p>(<b>a</b>) Geological map and (<b>b</b>) geomorphological map of Lefkas Island.</p>
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<p>(<b>a</b>) Geological map and (<b>b</b>) geomorphological map of Kefalonia Island.</p>
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<p>(<b>a</b>) Geological map and (<b>b</b>) geomorphological map of Zakynthos Island.</p>
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<p>Lithostratigraphic columns of (<b>a</b>) Ionian Basin and (<b>b</b>) Apulian Platform margins.</p>
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<p>Four cross-sections depict the major structures based on Google Earth Relief. For the abbreviations see the text and for the locations see <a href="#geosciences-14-00203-f001" class="html-fig">Figure 1</a>.</p>
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<p>Εvolutionary stages of development from rifting stage to present, applied to Paxoi and Anti-Paxoi Islands. (<b>a</b>) The rifting stage; (<b>b</b>) the change of the extensional to compressional regime with the reactivation of normal faults as reverse faults (inverted tectonic) and the gradual change from the Apulian platform margins to the forebulge area of the Ionian foreland and (<b>c</b>) the present morphology of Paxoi and Anti-Paxoi Islands with an open anticline geometry due to the Ionian thrust movement [<a href="#B37-geosciences-14-00203" class="html-bibr">37</a>].</p>
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<p>The block diagram illustrates how the tectonic inversion influenced the Apulian Platform Margins (APM) producing small, restricted foreland basins.</p>
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24 pages, 26872 KiB  
Article
Opening and Post-Rift Evolution of Alpine Tethys Passive Margins: Insights from 1D Numerical Modeling of the Jurassic Mikulov Formation in the Vienna Basin Region, Austria
by Darko Spahić, Eun Young Lee, Aleksandra Šajnović and Rastimir Stepić
Geosciences 2024, 14(8), 202; https://doi.org/10.3390/geosciences14080202 - 30 Jul 2024
Viewed by 598
Abstract
This study employed 1D numerical pseudo models to examine the Upper Jurassic carbonate succession, focusing on the Mikulov Formation in the Vienna Basin region. It addresses the protracted and complex history of the Jurassic source rock play, revealing a transition from rapid syn-rift [...] Read more.
This study employed 1D numerical pseudo models to examine the Upper Jurassic carbonate succession, focusing on the Mikulov Formation in the Vienna Basin region. It addresses the protracted and complex history of the Jurassic source rock play, revealing a transition from rapid syn-rift (>200 m/Ma) to slower post-rift sedimentation/subsidence of the overlying layers during extensional deformation (up to 120 m/Ma with a thickness of 1300 m). This provides valuable insights into the rift-to-drift stage of the central Alpine Tethys margin. The Mikulov marls exhibit characteristics of a post-rift passive margin with slow sedimentation rates. However, a crustal stretching analysis using syn-rift heat flow sensitivity suggested that thermal extension of the basement alone cannot fully explain the mid-Jurassic syn-rift stage in this segment of the Alpine Tethys. The sensitivity analysis showed that the mid-late Jurassic differential syn-rift sequences were exposed to slightly cooler temperatures than the crustal stretching model predicted. Heat flow values below 120 mW/m2 aligned with measurements from deeply settled Mesozoic successions, suggesting cold but short gravity-driven subsidence. This may account for the relatively low thermal maturation of the primary source rock interval identified by the time-chart analysis, despite the complex tectonic history and considerable sedimentary burial. The post-Mesozoic changes in the compaction trend are possibly linked to the compressional thrusting of the Alpine foreland and postdating listric faulting across the Vienna Basin. Full article
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<p>(<b>a</b>) Tectonic outline of the Alpine–Carpathian system in central, eastern, and southern Europe, encompassing the Vienna Basin (VB) and surrounding regions (Molasse Zone, Waschberg Unit (WU), Flysch Zone, Northern Calcareous Alps (NCA), and Central Alps). In the Vienna Basin, major fault lines on the pre-Neogene surface, including the Steinberg fault (SF) and the Vienna Basin Transfer Fault (VBTF), are shown (based on [<a href="#B9-geosciences-14-00202" class="html-bibr">9</a>,<a href="#B10-geosciences-14-00202" class="html-bibr">10</a>,<a href="#B11-geosciences-14-00202" class="html-bibr">11</a>]). (<b>b</b>) Geological profile (Section A) across the central Vienna Basin, Molasse Basin, and underlying tectonic units (revised from [<a href="#B12-geosciences-14-00202" class="html-bibr">12</a>]).</p>
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<p>(<b>a</b>) Paleogeography map of the Late Jurassic (~150 Ma), showing the Alpine Tethys, passive margin, and surroundings (red square). The area where the Late Jurassic carbonates were deposited and subsequently became part of the Vienna Basin’s basement is approximately indicated with a yellow square. The paleogeographical reconstruction is from [<a href="#B21-geosciences-14-00202" class="html-bibr">21</a>]. (<b>b</b>) Jurassic to Neogene development model of the Vienna Basin region (based on [<a href="#B22-geosciences-14-00202" class="html-bibr">22</a>,<a href="#B23-geosciences-14-00202" class="html-bibr">23</a>]).</p>
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<p>Stratigraphic framework, depositional environment, and tectonic phases of the Vienna Basin region, encompassing the autochthonous and allochthonous units and the Neogene sedimentary succession. Major petroleum system elements (PSEs) are indicated (based on [<a href="#B19-geosciences-14-00202" class="html-bibr">19</a>,<a href="#B22-geosciences-14-00202" class="html-bibr">22</a>,<a href="#B25-geosciences-14-00202" class="html-bibr">25</a>,<a href="#B26-geosciences-14-00202" class="html-bibr">26</a>,<a href="#B41-geosciences-14-00202" class="html-bibr">41</a>,<a href="#B42-geosciences-14-00202" class="html-bibr">42</a>,<a href="#B43-geosciences-14-00202" class="html-bibr">43</a>,<a href="#B44-geosciences-14-00202" class="html-bibr">44</a>,<a href="#B45-geosciences-14-00202" class="html-bibr">45</a>,<a href="#B46-geosciences-14-00202" class="html-bibr">46</a>]).</p>
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<p>(<b>a</b>) The structure map of the Vienna Basin, showing the faulted pre-Neogene basement surface with major oil and gas fields (modified from [<a href="#B11-geosciences-14-00202" class="html-bibr">11</a>,<a href="#B25-geosciences-14-00202" class="html-bibr">25</a>]). Locations of Zistersdorf Depression (ZD) and wells Maustrenk ÜT1 (M) and Zistersdorf ÜT (Z) are indicated. AT—Austria; SK—Slovakia; CZ—Czech Rep. (<b>b</b>) Geological cross-section across the Steinberg fault and Zistersdorf depression in the Vienna Basin, illustrating the autochthonous and allochthonous units and the assumed deeper basement structure. Major stratigraphic and structural units with well positions are marked (revised from [<a href="#B23-geosciences-14-00202" class="html-bibr">23</a>]).</p>
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<p>Geological cross-section providing a comprehensive view of the Alpine thrust sheets in the Vienna Basin region, derived from 2D seismic data (see <a href="#geosciences-14-00202-f001" class="html-fig">Figure 1</a>a for Section B location; based on [<a href="#B13-geosciences-14-00202" class="html-bibr">13</a>] and references therein). This section depicts the thrust units and zones characterized by active extensional detachment and Miocene sedimentation as well as out-of-sequence thrusting. In the context of our study, the detachment layers, predominantly comprised of Jurassic Mikulov Fm., are of particular importance.</p>
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<p>(<b>a</b>) Mikulov isopach map, depicting thickness beneath the central Vienna and its surrounding areas (revised from [<a href="#B14-geosciences-14-00202" class="html-bibr">14</a>]). (<b>b</b>) Vitrinite reflectance trend with increasing depth (data from [<a href="#B14-geosciences-14-00202" class="html-bibr">14</a>] and references therein).</p>
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<p>Boundary conditions for our models in PetroMod. (<b>a</b>) Standard heat flow values following the syn-rift stage based on the McKenzie model [<a href="#B39-geosciences-14-00202" class="html-bibr">39</a>], with an approximate value of 120 mW/m<sup>2</sup> from 190 Ma to 170 Ma. (<b>b</b>) Mean surface temperature, providing constraints on the temperatures at the bottom of the basin in southern Europe, based on data from [<a href="#B75-geosciences-14-00202" class="html-bibr">75</a>]. (<b>c</b>) Sediment–Water-Interface Temperature (SWIT) setting for southern Europe. (<b>d</b>) Paleowater depth (PWD) trends.</p>
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<p>Computed sedimentation rates by 1D pseudo modeling; different 1D input data of (<b>a</b>) 400 m, (<b>b</b>) 1300 m, and (<b>c</b>) 700 m in thickness.</p>
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<p>Time plots for computed parameters of the Mikulov Fm. from the Late Jurassic to the present day using different 1D input data; (<b>a</b>) 400 m, (<b>b</b>) 1300 m, and (<b>c</b>) 700 m thicknesses. The parameters shown include the maximum effective stress, layer thickness over time, sedimentation rate, and EASY%Ro. At the end of the Jurassic and the beginning of the Cretaceous, the sedimentation rates (blue line) showed an abrupt decrease; the computed layer thickness (pale red line) decreased due to compaction and tectonic exhumation; the thermal maturity (green line) remained low throughout the rifting, post-rift, and tectonic exhumation periods.</p>
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<p>Tests of crustal models relative to varying syn-rift heat flow values. Colors indicate present-day thermal maturation levels; yellow denotes overmature stages, red denotes the gas generation stage, green represents different stages of the oil window, and blue denotes the not matured sections. (<b>a</b>,<b>b</b>) show differences in the computed depth-related temperature for different Mesozoic formations.</p>
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<p>Time-depth diagrams based on the model’s analysis of Jurassic–Paleogene events, applying a thickness of 1300 m for the Mikulov Fm. (<b>a</b>) A 2D section model showing the change in computed subsurface heat flow according to paleowater depth input data (maximum water depth during the syn-rift). (<b>b</b>) Computed heat flow according to sedimentation rates. (<b>c</b>) Computed thermal maturity. Please note that the values were changed after the Paleocene events.</p>
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<p>(<b>a</b>) Approximation of Mikulov detachment during the onset of extension (Oligocene). PA1–3 indicate the differential pressures on the margin. (<b>b</b>) Extensional displacement following the remobilization of detachment. The remobilization is followed by the expulsion onset of fluids and gases (see green and red in (<b>a</b>)). The curve shows how the workaround considers the footwall downwelling of subthrust layers (at the expense of hanging wall upthrusting during the Alpine orogeny). (<b>c</b>) Parameters used for interpretation and calibration were temperature, burial history, effective porosity, bulk density, hydrocarbon generation pressure, overpressure, and lithostatic pressure.</p>
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14 pages, 2308 KiB  
Article
Petrophysical and Mechanical Properties of the Piromafo Stone Used in the Built Heritage of Apulia (SE Italy): A Comprehensive Laboratory Study
by Gioacchino Francesco Andriani
Geosciences 2024, 14(8), 201; https://doi.org/10.3390/geosciences14080201 - 29 Jul 2024
Viewed by 479
Abstract
Many historic buildings and monuments on the Salento Peninsula (Apulia, southern Italy) were built from locally quarried Miocene calcarenites belonging to the Pietra Leccese Formation (Late Burdigalian–early Messinian). The main facies consists of a homogeneous and porous biomicrite, pale yellow in colour and [...] Read more.
Many historic buildings and monuments on the Salento Peninsula (Apulia, southern Italy) were built from locally quarried Miocene calcarenites belonging to the Pietra Leccese Formation (Late Burdigalian–early Messinian). The main facies consists of a homogeneous and porous biomicrite, pale yellow in colour and fine- to medium-grained, very rich in planktonic Foraminifera and massive or thick-bedded in outcrop. Additionally, there are other facies, among which Piromafo stands out for its aesthetic appearance, enhanced by its greenish-brown or greenish-grey colours. Piromafo occurs in the upper part of the Pietra Leccese Fm. and is represented by a fine- to medium-grained glauconitic and phosphatic biomicrite with macrofossils, especially Bivalves and Gastropods. Despite its important historical use as a building and ornamental material, especially in Roman and Baroque architecture, a research gap exists in the scientific literature describing the properties of the stone and their correlation. Therefore, the aim of this paper is to present a wide range of properties useful in explaining the in situ behaviour and damage susceptibility of the stone in monuments and buildings, but also to assist in selecting preservation treatments and strategies. An overall assessment of the main petrophysical and mechanical properties, especially for restoration/conservation purposes, was performed using both standard and unconventional techniques. Starting with rock fabric inspection, particular attention was given to the relationship between the pore size distribution and the hydraulic and thermal properties of the material. Unconfined compressive strength, flexural strength, and indirect tensile strength were also estimated. The findings reveal a significant correlation between the pore size distribution and the hydraulic and thermal properties of Piromafo, impacting its durability and suitability for use in conservation. Specifically, the thermal properties, influenced by the mineral composition and fabric, indicate the potential for using Piromafo as an effective refractory and insulation material, which justifies the origin of its name and confirms what is already stated in the specific literature. Additionally, correlations were proposed among the various mechanical parameters evaluated, including the Schmidt hammer rebound values with compressive strength and tangent modulus. The mechanical analysis shows that the material possesses adequate properties for structural applications. Full article
(This article belongs to the Section Geoheritage, Geoparks and Geotourism)
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<p>The Roman Pier of San Cataldo (2nd century A.D.) in Lecce, Adriatic coast of Apulia (SE Italy).</p>
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<p>The geographic location of the main extraction areas of the Piromafo variety: (1) Cursi, Melpignano, Martano, Zollino, and Poggiardo; (2) Vernole, Vanze, Strudà, Acaya, and Pisignano; (3) Novoli and Surbo.</p>
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<p>The Mesoscopic appearances of the Piromafo specimens, respectively, from Strudà-Acaja (<b>a</b>) and Cursi-Melpignano (<b>b</b>). The length of view field for each photo is 5.4 cm.</p>
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<p>Microphotographs in plane polarized light from thin sections of the Piromafo: microscopic appearances of samples, respectively, from Strudà-Acaja (<b>a</b>) and Cursi-Melpignano (<b>b</b>). The length of view field for each photo is 2 mm.</p>
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<p>Cumulative intrusion/extrusion curves as a function of pore diameter measured by Mercury Intrusion Porosimetry (MIP) for two samples from the two extraction areas: (1) Cursi-Melpignano; (2) Strudà-Acaja.</p>
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<p>Pore size distribution obtained from a combination of the MIP (Mercury Intrusion Porosimetry) results and image and textural analysis, according to the method proposed by Andriani and Walsh [<a href="#B31-geosciences-14-00201" class="html-bibr">31</a>] and Andriani et al. [<a href="#B34-geosciences-14-00201" class="html-bibr">34</a>]. (<b>a</b>) Strudà-Acaja. (<b>b</b>) Cursi-Melpignano.</p>
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<p>Capillary water absorption experimental device (<b>a</b>) and results (<b>b</b>): (1) Cursi-Melpignano; (2) Strudà-Acaja.</p>
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<p>Typical stress–strain curves obtained for servo-controlled uniaxial compression tests on dry samples of the Piromafo variety: (1) Cursi-Melpignano; (2) Strudà-Acaja.</p>
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24 pages, 24217 KiB  
Article
Evaluating the Impact of DEM Spatial Resolution on 3D Rockfall Simulation in GIS Environment
by Maria P. Kakavas, Paolo Frattini, Alberto Previati and Konstantinos G. Nikolakopoulos
Geosciences 2024, 14(8), 200; https://doi.org/10.3390/geosciences14080200 - 29 Jul 2024
Viewed by 542
Abstract
Rockfalls are natural geological phenomena characterized by the abrupt detachment and freefall descent of rock fragments from steep slopes. These events exhibit considerable variability in scale, velocity, and trajectory, influenced by the geological composition of the slope, the topography, and other environmental conditions. [...] Read more.
Rockfalls are natural geological phenomena characterized by the abrupt detachment and freefall descent of rock fragments from steep slopes. These events exhibit considerable variability in scale, velocity, and trajectory, influenced by the geological composition of the slope, the topography, and other environmental conditions. By employing advanced modeling techniques and terrain analysis, researchers aim to predict and control rockfall hazards to prevent casualties and protect properties in areas at risk. In this study, two rockfall events in the villages of Myloi and Platiana of Ilia prefecture were examined. The research was conducted by means of HY-STONE software, which performs 3D numerical modeling of the motion of non-interacting blocks. To perform this modeling, input files require the processing of base maps and datasets in a GIS environment. Stochastic modeling and 3D descriptions of slope topography, based on Digital Elevation Models (DEMs) without spatial resolution limitations, ensure multiscale analysis capabilities. Considering this capability, seven freely available DEMs, derived from various sources, were applied in HY-STONE with the scope of performing a large number of multiparametric analyses and selecting the most appropriate and efficient DEM for the software requirements. All the necessary data for the multiparametric analyses were generated within a GIS environment, utilizing either the same restitution coefficients and rolling friction coefficient or varying ones. The results indicate that finer-resolution DEMs capture detailed terrain features, enabling the precise identification of rockfall source areas and an accurate depiction of the kinetic energy distribution. Further, the results show that a correct application of the model to different DEMs requires a specific parametrization to account for the different roughness of the models. Full article
(This article belongs to the Special Issue Earth Observation by GNSS and GIS Techniques)
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<p>Pictures of the rockfall events in the Myloi and Platiana region in relation to the Hellenic region.</p>
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<p>Pictures of the rockfall in the Myloi region illustrate the final position of rock blocks. Red arrows indicate the final positions of the rock masses at the conclusion of the rockfall event.</p>
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<p>Picture taken from the Platiana region after the rockfall event. The slope picture is taken from the Greek Cadastral (2008).</p>
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<p>Reforestation procedures in the slope next to the Platiana village. The slope pictures are taken from the Greek Cadastral.</p>
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<p>Myloi area before (<b>left</b> image) and after (<b>right</b> image) removing the vegetation through Cloud Compare Software.</p>
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<p>HY-STONE results for the site of Myloi using parameters (E<sub>N</sub>, E<sub>T</sub>, and A<sub>T</sub>) calibrated on the UAV DEM. The transit frequencies of blocks are shown for (<b>a</b>) UAV DEM, (<b>b</b>) Greek Cadastral DEM, (<b>c</b>) ALOS AW3D30 DEM, (<b>d</b>) ASTER GDEM, and (<b>e</b>) SRTM30 DEM.</p>
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<p>HY-STONE results for the site of Myloi using parameters (E<sub>N</sub>, E<sub>T</sub>, and A<sub>T</sub>) calibrated on the UAV DEM. The maximum translation kinetic energies are shown for (<b>a</b>) UAV DEM, (<b>b</b>) Greek Cadastral DEM, (<b>c</b>) ALOS AW3D30 DEM, (<b>d</b>) ASTER GDEM, and (<b>e</b>) SRTM30 DEM.</p>
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<p>Slope profiles from (<b>a</b>) UAV DSE, (<b>b</b>) Greek Cadastral DEM, (<b>c</b>) ALOS AW3D30 DEM, (<b>d</b>) ASTER GDEM, and (<b>e</b>) SRTM30 DEM.</p>
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<p>HY-STONE results, in terms of transit frequency for the site of Platiana. (<b>a</b>) Greek Cadastral DEM, (<b>b</b>) ALOS AW3D30 DEM, (<b>c</b>) ASTER GDEM, (<b>d</b>) SRTM30 DEM, (<b>e</b>) SRTM90 DEM, and (<b>f</b>) TanDEM_X.</p>
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<p>HY-STONE results in terms of the maximum translation kinetic energy for the site of Platiana. (<b>a</b>) Greek Cadastral DEM, (<b>b</b>) ALOS AW3D30 DEM, (<b>c</b>) ASTER GDEM, (<b>d</b>) SRTM30 DEM, (<b>e</b>) SRTM90 DEM, and (<b>f</b>) TanDEM_X.</p>
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<p>Slope profiles from (<b>a</b>) Greek Cadastral DEM, (<b>b</b>) ALOS AW3D30 DEM, (<b>c</b>) ASTER GDEM, (<b>d</b>) SRTM30 DEM, (<b>e</b>) SRTM90 DEM, and (<b>f</b>) TanDEM_X.</p>
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<p>HY-STONE results by using the Greek Cadastral DEM with the optimal coefficients shown in <a href="#geosciences-14-00200-t003" class="html-table">Table 3</a> for the site of Myloi: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>HY-STONE results by using the ALOS AW3D30 DEM with optimal coefficients shown in <a href="#geosciences-14-00200-t004" class="html-table">Table 4</a> for the site of Platiana: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>HY-STONE results by using the ASTER GDEM with the optimal coefficients shown in <a href="#geosciences-14-00200-t005" class="html-table">Table 5</a> for the site of Platiana: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>HY-STONE results by using the SRTM30 DEM with the optimal coefficients shown in <a href="#geosciences-14-00200-t006" class="html-table">Table 6</a> for the site of Platiana: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>HY-STONE results by using the SRTM90 DEM with the optimal coefficients shown in <a href="#geosciences-14-00200-t007" class="html-table">Table 7</a> for the site of Platiana: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>HY-STONE results by using the TanDEM_X with the optimal coefficients shown in <a href="#geosciences-14-00200-t008" class="html-table">Table 8</a> for the site of Platiana: (<b>a</b>) the transit frequency and (<b>b</b>) the maximum translation kinetic energy.</p>
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<p>The effect of spatial resolution in the normal (E<sub>N</sub>) and tangential (E<sub>T</sub>) restitutions and the rolling friction (A<sub>T</sub>) coefficients. For 30 m and 90 m, three and two DEMS are available, respectively.</p>
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24 pages, 3959 KiB  
Article
The Perspective of Using Neural Networks and Machine Learning Algorithms for Modelling and Forecasting the Quality Parameters of Coking Coal—A Case Study
by Artur Dyczko
Geosciences 2024, 14(8), 199; https://doi.org/10.3390/geosciences14080199 - 26 Jul 2024
Viewed by 434
Abstract
The quality of coking coal is vital in steelmaking, impacting final product quality and process efficiency. Conventional forecasting methods often rely on empirical models and expert judgment, which may lack accuracy and scalability. Previous research has explored various methods for forecasting coking coal [...] Read more.
The quality of coking coal is vital in steelmaking, impacting final product quality and process efficiency. Conventional forecasting methods often rely on empirical models and expert judgment, which may lack accuracy and scalability. Previous research has explored various methods for forecasting coking coal quality parameters, yet these conventional methods frequently fall short in terms of accuracy and adaptability to different mining conditions. Existing forecasting techniques for coking coal quality are limited in their precision and scalability, necessitating the development of more accurate and efficient methods. This study aims to enhance the accuracy and efficiency of forecasting coking coal quality parameters by employing neural networks and artificial intelligence algorithms, specifically in the context of Knurow and Szczyglowice mines. The research involves gathering historical data on various coking coal quality parameters, including a proximate and ultimate analysis, to train and test neural network models using the Group Method of Data Handling (GMDH). Real-world data from Knurow and Szczyglowice mines’ coal production facilities form the basis of this case study. The integration of neural networks and artificial intelligence techniques significantly improves the accuracy of predicting key quality parameters such as ash content, sulfur content, volatile matter, and calorific value. This study also examines the impact of these quality indicators on operational costs and highlights the importance of final indicators like the Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR) in expanding industrial reserve concepts. Model performance is evaluated using metrics such as mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate the effectiveness of these advanced techniques in enhancing predictive modeling in the mining industry, optimizing production processes, and improving overall operational efficiency. Additionally, this research offers insights into the practical implementation of advanced analytics tools for predictive maintenance and decision-making support within the mining sector. Full article
(This article belongs to the Topic Environmental Geology and Engineering)
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<p>Schedule of actions taken to develop proactive production control and stabilization of commercial coal parameters in JSW CG—source: [own study].</p>
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<p>The mining areas of JSW S.A. in relation to the surrounding mines (JSW S.A. materials).</p>
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<p>Scatter plots of factors potentially influencing the outcome variables: (<b>a</b>) Vitrinite reflectivity (RO) vs. CRI. (<b>b</b>) Dilatation (B) vs. CRI. (<b>c</b>) Moisture content (WA) vs. CSR.</p>
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<p>Actual data and model fit for CRI indicator under conditions of Knurow mine.</p>
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<p>Variation of residuals in GMDH-based neural network model for CRI indicator.</p>
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<p>Distribution of residuals in GMDH-based neural network model for CRI indicator.</p>
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<p>Actual data and model fit for CRI indicator under conditions of Szczyglowice mine.</p>
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<p>Distribution of residuals in Stepwise Forward Selection model for CRI indicator.</p>
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<p>Variation of residuals in Stepwise Forward Selection model for CRI indicator.</p>
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<p>Actual data and model fit for CSR indicator under conditions of Knurow mine.</p>
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<p>Variation of residuals in Stepwise Mixed Selection type of GMDH model for CSR indicator.</p>
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<p>Distribution of residuals in Stepwise Mixed Selection type of GMDH model for CSR indicator.</p>
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<p>Actual data and model fit for CSR indicator under conditions of Szczyglowice mine.</p>
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<p>Variation of residuals in GMDH-type neural network model for CSR indicator.</p>
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<p>Distribution of residuals in GMDH-type neural network model for CSR indicator.</p>
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31 pages, 13800 KiB  
Article
Analysis of Debris Flow Protective Barriers Using the Coupled Eulerian Lagrangian Method
by Shiyin Sha, Ashley P. Dyson, Gholamreza Kefayati and Ali Tolooiyan
Geosciences 2024, 14(8), 198; https://doi.org/10.3390/geosciences14080198 - 26 Jul 2024
Cited by 1 | Viewed by 463
Abstract
Protective structures play a vital role in mitigating the risks associated with debris flows, yet assessing their performance poses crucial challenges for their real-world effectiveness. This study proposes a comprehensive procedure for evaluating the performance of protective structures exposed to impacts from media [...] Read more.
Protective structures play a vital role in mitigating the risks associated with debris flows, yet assessing their performance poses crucial challenges for their real-world effectiveness. This study proposes a comprehensive procedure for evaluating the performance of protective structures exposed to impacts from media transported by large debris flow events. The method combines numerical modelling with site conditions for existing structures along the Hobart Rivulet in Tasmania, Australia. The Coupled Eulerian Lagrangian (CEL) model was validated by comparing simulation results with experimental data, demonstrating high agreement. Utilising three-dimensional modelling of debris flow–boulder interactions over the Hobart Rivulet terrain, boulder velocities were estimated for subsequent finite element analyses. Importantly, a model of interaction between boulders and I-beam posts was established, facilitating a comparative assessment of five distinct I-beam barrier systems defined as Type A to E, which are currently in use at the site. Simulation results reveal larger boulders display a slower increase in their velocities over the 3D terrain. Introducing a key metric, the failure ratio, enable a mechanism for comparative assessments of these barrier systems. Notably, the Type E barriers demonstrate superior performance due to fewer weak points within the structure. The combined CEL and FE assessments allow for multiple aspects of the interactions between debris flows, boulders, and structures to be considered, including structural failure and deformability, to enhance the understanding of debris flow risk mitigation in Tasmania. Full article
(This article belongs to the Section Natural Hazards)
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<p>Examples of debris flow events in Tasmania, Australia. (<b>a</b>) The 1872 Glenorchy debris flow event [<a href="#B23-geosciences-14-00198" class="html-bibr">23</a>], (<b>b</b>) 2007 Philps Peak debris flow event [<a href="#B31-geosciences-14-00198" class="html-bibr">31</a>], and (<b>c</b>) 2018 the Kunanyi/Mt Wellington debris flow event.</p>
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<p>Topographic map highlighting study area (red area) along the Hobart Rivulet (blue lines) (<b>top</b>); elevation profile with previous landslide locations (<b>bottom left</b>) [<a href="#B22-geosciences-14-00198" class="html-bibr">22</a>]; isometric view of the study area (<b>bottom right</b>).</p>
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<p>Examples of the existing protective measures along the Hobart Rivulet. (<b>a</b>) Type A: two rows of vertical posts, both with cross flanges; (<b>b</b>) type B: single row of vertical posts with a cross flange; (<b>c</b>) type C: Two rows of vertical posts, no cross flanges; (<b>d</b>) type D: two rows of vertical posts, with a cross flange in the front row only.</p>
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<p>The overview of the proposed performance assessment process.</p>
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<p>Model configuration for simulating the boulder-enriched debris flow tests [<a href="#B21-geosciences-14-00198" class="html-bibr">21</a>].</p>
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<p>Comparison of experimental and CEL simulation results. (<b>a</b>) Time history of impact force for test D; (<b>b</b>) velocities of debris front and first arrival boulder.</p>
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<p>The CEL model of the Hobart Rivulet. (<b>a</b>) The model configuration (using the model with 1 m cylindrical boulders as an example); (<b>b</b>) Eulerian domain and initial debris flow.</p>
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<p>The estimation of boulder velocities on the Hobart Rivulet terrain. (<b>a</b>) Variations in velocities of the first boulder for each case with displacement in the x-direction; (<b>b</b>) comparison of the first boulder’s arrival time and boulder speed at the arrival time.</p>
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<p>Variations in accelerations of the first boulder for each case with displacement in x-direction on the Hobart Rivulet terrain: (<b>a</b>) C1; (<b>b</b>) C2; (<b>c</b>) C3; (<b>d</b>) C4; (<b>e</b>) C5; (<b>f</b>) C6.</p>
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<p>The model of type A barrier and beam number for the performance analysis.</p>
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<p>The model of type B barrier and beam number for the performance analysis.</p>
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<p>The model of type C barrier and beam number for the performance analysis.</p>
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<p>The model of type D barrier and beam number for the performance analysis.</p>
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<p>The model of type E barrier and beam number for the performance analysis.</p>
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<p>The model of boulder–structure interaction—centroid indicated in red (rest AN1 was used as an example): the geometry of the model (<b>top</b>) and side view of the model for one I-beam post (<b>bottom</b>).</p>
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<p>Visualisation of variations in failed elements with simulation time for case EFN9.</p>
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<p>The failure ratio of each test for type A barrier.</p>
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<p>The failure ratio of each test for type B barrier.</p>
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<p>The failure ratio of each test for type C barrier.</p>
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<p>The failure ratio of each test for type D barrier.</p>
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<p>The failure ratio of each test for type E barrier.</p>
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<p>Comparison of the maximum failure ratio for five types of I-beam barriers.</p>
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<p>Comparison of the reaction force and displacement in X-direction between the I-beams DFN6 and EFN9; (<b>a</b>) variations in reaction force with simulation time; (<b>b</b>) variations in displacement with simulation time.</p>
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17 pages, 4646 KiB  
Article
A Unique Conditions Model for Landslide Susceptibility Mapping
by Florimond De Smedt and Prabin Kayastha
Geosciences 2024, 14(8), 197; https://doi.org/10.3390/geosciences14080197 - 24 Jul 2024
Viewed by 550
Abstract
Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic [...] Read more.
Several methods and approaches have been proposed to assess landslide susceptibility. The likelihood of landslides occurring can be determined by applying statistical models to historical landslides, taking into account controlling factors. Popular methods for predicting the probability of landslides are weights-of-evidence and logistic regression. We discuss the assumptions and interpretations of these methods, the relationships between them, and their strengths and weaknesses in case of categorical factors. Of particular interest is the conditional independence of the controlling factors and its effect on model bias. To avoid lack of conditional independence of factors and model bias, we present a unique conditions model that is always unbiased. To illustrate the theoretical developments, a practical application is given using observed landslides and geo-environmental factors from a previous study. The unique conditions model appears superior to the other models. Full article
(This article belongs to the Special Issue Landslide Monitoring and Mapping II)
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Figure 1

Figure 1
<p>Raster maps of the observed landslides indicator <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math> and the controlling factors <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>x</mi> </mrow> <mrow> <mi>i</mi> <mi>j</mi> </mrow> </msub> </mrow> </semantics></math> the legend labels of the factor classes are listed in the same order as in Table 2.</p>
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<p>Workflow for the unique conditions model explained in pseudocode.</p>
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<p>Raster maps of the posterior landslide probability obtained with weights-of-evidence, logistic regression, and unique conditions model.</p>
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<p>ROC curves obtained using weights-of evidence, logistic regression, and the unique conditions model.</p>
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<p>Map of the landslide susceptibility index (LSI) obtained with the unique conditions model.</p>
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12 pages, 6717 KiB  
Article
Identification and Verification of the Movement of the Hidden Active Fault Using Electrical Resistivity Tomography and Excavation
by Rungroj Arjwech, Sutatcha Hongsresawat, Suriyachai Chaisuriya, Jetsadarat Rattanawannee, Pitsanupong Kanjanapayont and Winit Youngme
Geosciences 2024, 14(8), 196; https://doi.org/10.3390/geosciences14080196 - 24 Jul 2024
Viewed by 456
Abstract
Identifying the movement of the branches of the hidden Thakhek fault in Thailand is challenging due to the absence of evident landforms indicating an active fault. In this study, we analyzed a digital elevation model (DEM) to identify potential landforms. A 2D Electrical [...] Read more.
Identifying the movement of the branches of the hidden Thakhek fault in Thailand is challenging due to the absence of evident landforms indicating an active fault. In this study, we analyzed a digital elevation model (DEM) to identify potential landforms. A 2D Electrical Resistivity Tomography (ERT) survey was conducted to locate the hidden Thakhek fault. The results reveal vivid images of resistivity contrast, interpreted as two reverse faults, with mudstone exhibiting low resistivity in the middle, flanked by thick sediment layers with higher resistivity. Three trenches were excavated perpendicular to the two interpreted reverse faults. The displacement of reverse faulting appears to have shifted mudstone over Quaternary sediments, with vertical offsets revealed in trenches NWY-1, NWY-2, and NWY-3. This movement could be identified as a positive flower structure. Additionally, lakes are identified as a negative flower structure along the traces. These features result from strike-slip strains under a locally appropriate compressional and extensional environment in a shearing strike-slip fault. Full article
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Figure 1
<p>Interpretation of the Thakhek fault using DEM, showing major faults extending from Laos into Thailand. The TB1 and TB2 traces are inferred to branch from the main strike-slip fault, extending into Northeast Thailand with a general NW–SE orientation. The locations of Figures 3 and 5 are indicated on TB1 trace [<a href="#B9-geosciences-14-00196" class="html-bibr">9</a>].</p>
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<p>Geologic map of study area in Northeast Thailand and western Laos with the indicated locations of Figure 5a and a cross-section presenting interpreted faults (modified from [<a href="#B2-geosciences-14-00196" class="html-bibr">2</a>]).</p>
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<p>Two-dimensional ERT tomogram of profile BK01 (<b>a</b>) displaying resistivity tomography based on the L1–norm (robust) inversion method. The images show mudstone (low resistivity) flanked by coarse-grained sediment layers (high resistivity). Profile BK02 (<b>b</b>) displays a sharp contact between mudstone and sediment layers.</p>
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<p>Two-dimensional ERT tomogram of profile BK03 displaying resistivity tomography based on the L1–norm (robust) inversion method. The image shows coarse-grained sediment layers (high resistivity) flanked by mudstone (low resistivity).</p>
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<p>Photographs displaying the deployment of the 2D ERT profiles BK02 (<b>a</b>) and BK03 (<b>b</b>). An overview of the trench locations (<b>c</b>) and exposed walls of the NWY-1, with an inset showing the size of gravel compared to a water bottle (<b>d</b>), and NWY-2 (<b>e</b>) trenches. The inset photo in (<b>e</b>) shows a close-up view of the contact between mudstone and sediments, depicting the grain size distribution of unconsolidated sediments, while (<b>f</b>) shows the exposed walls of the NWY-3.</p>
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<p>Photographs of the trench walls in NWY-1 (<b>a</b>) and NWY-2 (<b>c</b>), along with the stratigraphy of NWY-1 (<b>b</b>) and NWY-2 (<b>d</b>) trenches. Thrust faulting results in a main strike slip movement characterized by as a positive flower structure and antiform (<b>e</b>).</p>
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<p>Photograph of the trench wall in NWY-3 (<b>a</b>) along with the stratigraphy (<b>b</b>).</p>
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