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15 pages, 4551 KiB  
Article
Tracking the Role of Faults on Mudstone Caprock Seals: A Case Study from Beier Depression, Hailar Basin, NE China
by Xin Liu, Yuexiang Li, Linlin Yang, Taohua He, Ya Zhao, Qianghao Zeng, Jiayi He and Guang Fu
Processes 2024, 12(10), 2221; https://doi.org/10.3390/pr12102221 - 11 Oct 2024
Viewed by 296
Abstract
To study the oil and gas enrichment characteristics of the reservoir rocks in the second Member of the Nantun Formation (N2) of the F1 fault in the Beier Depression, this research focuses on the mechanism of shortening the sealing time [...] Read more.
To study the oil and gas enrichment characteristics of the reservoir rocks in the second Member of the Nantun Formation (N2) of the F1 fault in the Beier Depression, this research focuses on the mechanism of shortening the sealing time of the regional mudstone cap due to faults and the factors influencing the sealing duration. The period during which the regional mudstone cap seal began is determined by analyzing the relationship between the diagenetic index of the regional mudstone cap and its underlying reservoir rocks and time. This relationship between the rock-formation index and time helps establish the onset of regional mudstone cap seal, and from this, the index of shortening time due to faults is derived. A research framework to study the sealing time shortening of regional mudstone cap seal caused by a fault was developed, which was further applied to analyze the relationship between the fault-induced shortening time of the seal in the lower of the first Member of the Damoguaihe Formation section (D1x) of the Beier Depression and hydrocarbon concentrations in the N2 Formation. The results show that the F1 fault at measurement points 1–4, 6, and 9–11 reduced the blocking time of the regional mudstone cap in D1x to 100%, hindering hydrocarbon accumulation and preservation in the N2. Conversely, the F1 fault at measurement points 5, 7, 8, and 12–15 reduces the blocking time of the regional mudstone cap in the D1x by 37–99%, which is more conducive to hydrocarbon accumulation and preservation in N2, resulting in positive oil and gas shows. Based on the test results, a discussion on the feasibility, applicability, and limitations of the new method is conducted, yielding the following conclusions: (1) The relationship between oil and gas indications in the reservoir rocks of the Nan’er Member confirms the feasibility of the new method. The research findings of the new method on the F1 fault align with current oil and gas exploration realities, indicating its potential use in studying the degree to which faults shorten the sealing time of regional mudstone caprocks. (2) The new method is primarily applicable to the study of the modification effects of tensile normal faults on regional mudstone caprocks and their impact on oil and gas sealing capacities. However, its application to other types of faults may have limitations. (3) The new method is mainly suitable for caprocks dominated by mudstone. When studying other types of caprocks, such as carbonate rocks, evaporites, igneous rocks, and metamorphic rocks, different research methods and technical means are required. These findings provide valuable insights for oil and gas exploration efforts near fault zones. Full article
(This article belongs to the Section Energy Systems)
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Figure 1

Figure 1
<p>Regional location map of the Hailar Basin (<b>a</b>), Tectonic units of Hailar Basin (<b>b</b>), Beier Depression (<b>c</b>), and stratigraphic column (<b>d</b>).</p>
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<p>Oil and gas distribution map of the N<sub>2</sub> section of F<sub>1</sub> fault at different measuring points. (<b>a</b>) Plane figure. (<b>b</b>) Sectional drawing. F<sub>1</sub>: Fault name. B9–B78: well names. T<sub>2</sub>–T<sub>3</sub>: Seismic reflection. D<sub>1</sub>: Damoguaihe Formation Member I. D<sub>2</sub>: Damoguaihe Formation Member II. N<sub>1</sub>: Nantun Formation Member I. N<sub>2</sub>: Nantun Formation Member II.</p>
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<p>Schematic diagram of the disconnected thickness of the caprock.</p>
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<p>Schematic diagram of the relationship between the regional mudstone caprock and the closed formation period of the fault rock in it.</p>
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<p>Schematic diagram for determining the formation time of regional mudstone cap and its internal fault rock blocking.</p>
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<p>Schematic diagram for determining the formation time of regional mudstone cap and its internal fault rock seal.</p>
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<p>Time determination map of the formation of regional mudstone cap and its F<sub>1</sub> fault rock sealing property in <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">D</mi> <mn>1</mn> <mi mathvariant="normal">x</mi> </msubsup> </mrow> </semantics></math> section. (<b>a</b>) Measurement Point 5. (<b>b</b>) Measurement Point 7. (<b>c</b>) Measurement Point 8. (<b>d</b>) Measurement Point 12. (<b>e</b>) Measurement Point 13. (<b>f</b>) Measurement Point 14. (<b>g</b>) Measurement Point 15.</p>
Full article ">Figure 7 Cont.
<p>Time determination map of the formation of regional mudstone cap and its F<sub>1</sub> fault rock sealing property in <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">D</mi> <mn>1</mn> <mi mathvariant="normal">x</mi> </msubsup> </mrow> </semantics></math> section. (<b>a</b>) Measurement Point 5. (<b>b</b>) Measurement Point 7. (<b>c</b>) Measurement Point 8. (<b>d</b>) Measurement Point 12. (<b>e</b>) Measurement Point 13. (<b>f</b>) Measurement Point 14. (<b>g</b>) Measurement Point 15.</p>
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<p>The relationship between short sealing time of regional mudstone cap in <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">D</mi> <mn>1</mn> <mi mathvariant="normal">x</mi> </msubsup> </mrow> </semantics></math> section caused by F<sub>1</sub> fault and oil and gas enrichment.</p>
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23 pages, 10947 KiB  
Review
The Role of Organic Matter and Hydrocarbons in the Genesis of the Pb-Zn-Fe (Ba-Sr) Ore Deposits in the Diapirs Zone, Northern Tunisia
by Larbi Rddad, Nejib Jemmali and Samar Jaballah
Minerals 2024, 14(9), 932; https://doi.org/10.3390/min14090932 - 12 Sep 2024
Viewed by 1019
Abstract
Extensional tectonics along NE-trending faults, coupled with diapirism, created paleo-highs and subsiding basins, providing the structural framework for subsequent mineralization processes. The preservation of organic matter within the Fahdene and Bahloul Cretaceous formations during the Anoxic Oceanic Events (AOE-1 and AOQ-2) facilitated the [...] Read more.
Extensional tectonics along NE-trending faults, coupled with diapirism, created paleo-highs and subsiding basins, providing the structural framework for subsequent mineralization processes. The preservation of organic matter within the Fahdene and Bahloul Cretaceous formations during the Anoxic Oceanic Events (AOE-1 and AOQ-2) facilitated the extraction of metals from seawater. The association of metals with organic matter, Fe-Mg oxides, and pyrite is revealed by principal component analysis (PCA). The subsequent maturation of organic matter generated hydrocarbons, with thermal cracking leading to the incorporation of organo-metallic ligands into mobile hydrocarbons. Oilfield brines form as a byproduct of this catagenesis. The metal-rich hydrocarbons and basinal brines invaded SO4−2-rich fluids from Triassic evaporites, resulting in the precipitation of sulfates (barite and celestite) and the bacteriogenic (BSR) and/or thermal (TSR) reduction of sulfate to reduced sulfur, which combined with metals to form sulfide ores. This study examines the role of hydrocarbons in the genesis of ore deposits within the diapiric zone, drawing upon a synthesis of literature and geological data. It highlights the interplay between basinal evolution, the organic matter-rich Cretaceous formations (Fahdene and Bahloul), diapiric paleo-highs, and the Alpine orogeny, which are identified as crucial factors in ore genesis in the diapiric zone. Full article
(This article belongs to the Special Issue The Role of Hydrocarbons in the Genesis of Mineral Deposits)
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Figure 1
<p>Simplified geologic map of northern Tunisia (modified from [<a href="#B28-minerals-14-00932" class="html-bibr">28</a>,<a href="#B29-minerals-14-00932" class="html-bibr">29</a>,<a href="#B30-minerals-14-00932" class="html-bibr">30</a>]) with distribution of ore deposits, magmatic rocks, and deep-seated faults.</p>
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<p>Cross section passing by Nappes Zone, Diapirs Zone, Northern Atlas, and Foreland basin, Tunisia (modified from [<a href="#B52-minerals-14-00932" class="html-bibr">52</a>,<a href="#B53-minerals-14-00932" class="html-bibr">53</a>,<a href="#B54-minerals-14-00932" class="html-bibr">54</a>,<a href="#B55-minerals-14-00932" class="html-bibr">55</a>]). The cross section passes along line A-B in <a href="#minerals-14-00932-f001" class="html-fig">Figure 1</a>.</p>
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<p>Synthetic lithostratigraphic log showing the rocks series from the Triassic to the Late Cretaceous, Diapirs Zone, Tunisia (modified from [<a href="#B47-minerals-14-00932" class="html-bibr">47</a>]).</p>
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<p>Representative selected ore Type 2 stratiform within the Bahloul Formation from the Diapirs zone Pb-Zn deposits. (<b>A</b>) and (<b>B</b>) Stratiform Zn-rich ore of Bou Grine cut by veinlets of sphalerite or veins of calcite; (<b>C</b>) stratiform sphalerite with pyrite of Bou Grine cut by veinlets of crystalline sphalerite and calcite; (<b>D</b>) banded Zn-rich ore of Kebbouch South within sphalerite and galena; (<b>E</b>,<b>F</b>) stratiform Zn-rich ore of Kebbouch South, with fine-grained sphalerite cut by crystalline sphalerite and galena. L: limestone; Gn: galena; Sp: sphalerite; Py: pyrite; Ca: calcite.</p>
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<p>Representative selected ore Group 1 hosted in the strata-bound transition zone from the Diapirs zone Pb-Zn deposits. (<b>A</b>) Banded Zn-rich sulfide with sphalerite and galena of Fedj-el-Adou; (<b>B</b>) vein Pb-rich sulfide with galena and minor sphalerite and nacrite of Fedj-el-Adoum; (<b>C</b>) massive Pb-rich sulfide with galena, a minor amount of sphalerite and calcite of Fedj-el-Adoum; (<b>D</b>) massive pyrite with galena and calcite of Kebbouch South; (<b>E</b>) massive pyrite with sphalerite of Kebbouch South; (<b>F</b>) drill core shows massive pyrite in breccia dolostone of Kebbouch South; (<b>G</b>) breccia dolostone with galena and calcite of Sakiet Koucha; (<b>H</b>,<b>I</b>) brecciated black dolostones with sphalerite, galena, calcite, and celestite of Sakiet Koucha (photo <b>I</b>). D: dolostone; Gn: galena; Sp: sphalerite; Py: pyrite; Ca: calcite; Cel: celestite.</p>
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<p>Representative selected ore of Group 3 hosted in Cretaceous rocks from the Diapirs zone Pb-Zn deposits. (<b>A</b>) Veins of galena and calcite crosscutting the Bahloul Formation limestones in Kebbouch South; (<b>B</b>) sphalerite, pyrite, and calcite in veins in the Bahloul Formation of Bou Grine; (<b>C</b>) stockwork mineralization with sphalerite, galena, and calcite in the Bahloul Formation of Sakiet-Koucha; (<b>D</b>,<b>E</b>) veinlets of galena and sphalerite and veins of calcite crosscutting the Bahloul Formation of El Akhouat; (<b>F</b>) disseminated galena and veins of calcite in the Bahloul Formation of Guarn–Halfaya; (<b>G</b>) dolomitized limestone of the Abiod Formation with mainly sphalerite of Boukhil; (<b>H</b>) limestone of the Abiod Formation with mainly veinlets of galena of Boukhil; (<b>I</b>) galena and sphalerite with clayey limestone of the Abiod Formation of Boukhil; (<b>J</b>) disseminated galena and in veins associated with barite in the in the Serdj Formation brecciated zone of Jebel Slata, Sidi Amor; (<b>K</b>) coarse-grained galena with barite in the Serdj Formation brecciated zone of Jebel Slata, Sidi Amor; (<b>L</b>) veins with galena and barite hosted in the Serdj Formation of Jebel Slata, Sidi Amor. DL: dolomitized limestone; L: limestone; CL: clayey limestone; Gn: galena; Sp: sphalerite; Py: pyrite; Ca: calcite; Ba: barite.</p>
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<p>Oil seeps in the dolomitized limestone (DL) of the Abiod Formation encountered in an underground mine in Boukhil.</p>
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<p>Biplot graphs displaying loading values for total organic carbon and trace elements in the Bahloul Formation of Guarn–Halfaya and the Fahdene Formation of Slata from the dataset of Rddad et al. [<a href="#B14-minerals-14-00932" class="html-bibr">14</a>]. PC1 vs. PC2 (<b>A</b>) and PC1 vs. PC3 (<b>B</b>) for the Bahloul Formation; PC1 vs. PC2 (<b>C</b>) and PC1 vs. PC3 (<b>D</b>) for the Fahdene Formation. The blue arrow is used to indicate whether trace elements are associated with organic matter or not.</p>
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<p>Biplot graphs displaying loading values for total major and trace elements in the Bahloul Formation of Guarn–Halfaya and Fahdene Formation of Slata from the dataset of Rddad et al. [<a href="#B14-minerals-14-00932" class="html-bibr">14</a>]. PC1 vs PC2 (<b>A</b>) and PC1 vs PC3 (<b>B</b>) for the Bahloul Formation and PC1 vs PC2 (<b>C</b>) and PC1 vs PC3 (<b>D</b>) for the Fahdene Formation. The arrows are used to indicate the oxide phase(s) to which trace elements are associated.</p>
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<p>Plots of <sup>208</sup>Pb/<sup>204</sup>Pb vs. <sup>206</sup>Pb/<sup>204</sup>Pb (<b>A</b>) and <sup>207</sup>Pb/<sup>204</sup>Pb vs. <sup>206</sup>Pb/<sup>204</sup>Pb (<b>B</b>) for galena samples of Groups 1, 2, and 3 from selected diapiric-related ore deposits [<a href="#B27-minerals-14-00932" class="html-bibr">27</a>,<a href="#B36-minerals-14-00932" class="html-bibr">36</a>,<a href="#B50-minerals-14-00932" class="html-bibr">50</a>], as well as for Cretaceous rocks [<a href="#B72-minerals-14-00932" class="html-bibr">72</a>] and Miocene igneous rocks [<a href="#B45-minerals-14-00932" class="html-bibr">45</a>]. The curves depicting growth trends for Pb isotope ratios are from the plumbotectonic model of [<a href="#B73-minerals-14-00932" class="html-bibr">73</a>].</p>
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<p>A schematic conceptual model illustrating the ore genesis in the diapiric zone. (<b>A</b>) Jurassic extension and the formation of the fault-bounded graben basins; (<b>B</b>) Cretaceous tectonics with diapirism along the major faults and the accumulation of organic matter-rich Cretaceous formations (Fahdene and Bahloul); (<b>C</b>) close-up schematic model showing trace element fixation by the inorganic and organic fractions in organic matter-rich formations during the Cretaceous; (<b>D</b>) alpine orogeny with the migration of metal-rich hydrocarbons and the accompanying basinal brines to the loci of deposition in the diapiric paleo-highs.</p>
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<p>Schematic model illustrating the migration of metal-rich hydrocarbons and associated basinal brines towards diapiric paleo-highs, with ore precipitation primarily occurring along the SE side of the diapir.</p>
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18 pages, 3399 KiB  
Article
A New Mineral Calcioveatchite, SrCaB11O16(OH)5·H2O, and the Veatchite–Calcioveatchite Isomorphous Series
by Igor V. Pekov, Natalia V. Zubkova, Vladimir N. Apollonov, Vasiliy O. Yapaskupt, Sergey N. Britvin and Dmitry Yu. Pushcharovsky
Minerals 2024, 14(9), 901; https://doi.org/10.3390/min14090901 - 2 Sep 2024
Viewed by 445
Abstract
The new mineral calcioveatchite, ideally SrCaB11O16(OH)5·H2O, is a Ca-Sr-ordered analogue of veatchite. It was found at the Nepskoe potassium salt deposit, Irkutsk Oblast, Siberia, Russia in halite-sylvite and sylvite-carnallite rocks, with boracite, hilgardite, kurgantaite, hydroboracite, [...] Read more.
The new mineral calcioveatchite, ideally SrCaB11O16(OH)5·H2O, is a Ca-Sr-ordered analogue of veatchite. It was found at the Nepskoe potassium salt deposit, Irkutsk Oblast, Siberia, Russia in halite-sylvite and sylvite-carnallite rocks, with boracite, hilgardite, kurgantaite, hydroboracite, volkovskite, veatchite, anhydrite, magnesite, and quartz. Calcioveatchite forms prismatic or tabular crystals up to 1 × 1.5 × 3 mm3 and crystal clusters up to 3 mm across. It is transparent and colourless with vitreous lustre. Calcioveatchite is brittle, cleavage is perfect on {010}, the Mohs’ hardness is ca 2, Dmeas is 2.58(1), and Dcalc is 2.567 g cm−3. Calcioveatchite is optically biaxial (+), α = 1.543(2), β = 1.550(5), γ = 1.626(2), 2Vmeas = 30(10)°, and 2Vcalc = 35°. The average chemical composition (wt.%, electron microprobe, H2O calculated by stoichiometry) is: CaO 7.05, SrO 20.70, B2O3 61.96, H2O 10.22, and total 99.93. The empirical formula, calculated based on 22 O apfu = O16(OH)5(H2O) pfu, is Sr1.23Ca0.78B10.99O16(OH)5·H2O. Calcioveatchite is monoclinic, space group P21, a = 6.7030(3), b = 20.6438(9), c = 6.6056(3) Å, β = 119.153(7)°, V = 798.26(8) Å3, and Z = 2. Polytype: 1M. The strongest reflections of the powder XRD pattern [d,Å(I,%)(hkl)] are: 10.35(100)(020), 5.633(12)(110), 5.092(10)(120), 3.447(14)(060), 3.362(13)(101, 051), 3.309(38)(–102), 2.862(10)(012), and 2.585(19)(080). The crystal structure was solved based on single-crystal XRD data, R1 = 0.0420. Calcioveatchite (calcioveatchite-1M) is an isostructural analogue of veatchite-1M with the 11-fold cation polyhedron occupied mainly by Sr [Sr0.902(8)Ca0.098(8)] whereas the 10-fold polyhedron is Ca dominant [Ca0.686(7)Sr0.314(7)]. The chemical composition of veatchite from five localities in Russia (Nepskoe), Kazakhstan (Shoktybay and Chelkar in the North Caspian Region), and the USA (Tick Canyon and Billie Mine in California) was studied, and it is shown to exist in nature as a continuous, almost complete isomorphous series which extends from Ca-free veatchite, Sr2B11O16(OH)5·H2O, to calcioveatchite with the composition Sr1.14Ca0.87B10.99O16(OH)5·H2O. Full article
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Figure 1
<p>Crystals of calcioveatchite. FOV width is 6 mm.</p>
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<p>Single crystals (<b>a</b>–<b>c</b>) and crystal cluster (<b>d</b>) of calcioveatchite. SEM (SE) images.</p>
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<p>Blocky crystal of calcioveatchite intergrown with aggregate small crystals of boracite (dark divergent tetrahedra) and anhydrite (light bar-shaped crystals). SEM (BSE) image.</p>
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<p>Typical crystals of calcioveatchite.</p>
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<p>Powder infrared absorption spectra of (<b><span class="html-italic">a</span></b>) veatchite from Shoktybay, Western Kazakhstan, and (<b><span class="html-italic">b</span></b>) holotype calcioveatchite.</p>
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<p>The atomic Sr–Ca ratio in minerals of the calcioveatchite–veatchite isomorphous series: 1–2—calcioveatchite from Nepskoe (2—holotype); 3–7—veatchite: 3—Nepskoe, 4—Shoktybay, 5—Chelkar, 6—Tick Canyon, 7—Billie Mine.</p>
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<p>Layer of B-centred polyhedra without additional [B(OH)<sub>3</sub>] triangle (i.e., FBB II—see text) in the structure of calcioveatchite (<b>a</b>) and the sequence of FBBs I and II (<b>b</b>). H atoms are shown as small black circles. The unit cell is outlined in (<b>b</b>).</p>
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<p>The crystal structure of calcioveatchite projected along the <span class="html-italic">a</span> axis. B-centred polyhedra are red, H atoms are small black circles, and O atoms of H<sub>2</sub>O molecules are blue circles. The unit cell is outlined.</p>
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20 pages, 6033 KiB  
Article
Identification of Anthropogenic and Natural Inputs of Sulfate into River System of Carbonate Zn-Pb Mining Area in Southwest China: Evidence from Hydrochemical Composition, δ34SSO4 and δ18OSO4
by Kailiang Zhang, Zeming Shi, Xiaoyan Ding, Liquan Ge, Maolin Xiong, Qingxian Zhang, Wanchang Lai and Liangquan Ge
Water 2024, 16(16), 2311; https://doi.org/10.3390/w16162311 - 16 Aug 2024
Viewed by 536
Abstract
The release of pollutants from lead-zinc mining areas poses a significant threat to the environment, making pollution tracing crucial for environmental protection. However, the complexity of carbonate mining areas makes tracing these pollutants challenging. This study used δ34SSO4 and δ [...] Read more.
The release of pollutants from lead-zinc mining areas poses a significant threat to the environment, making pollution tracing crucial for environmental protection. However, the complexity of carbonate mining areas makes tracing these pollutants challenging. This study used δ34SSO4 and δ18OSO4 isotopes combined with the Stable Isotope Mixing Models in R (SIMMR) to assess anthropogenic sulfate sources in the Daliangzi mining area. The river water types were mainly Ca2+-Mg2+-HCO3, and SO42, which are significantly influenced by dolomite dissolution. The δ34SSO4 values ranged from 6.47‰ to 17.96‰ and the δ18OSO4 values ranged from −5.66‰ to 13.98‰. The SIMMR results showed that evaporite dissolution in tributaries, driven by gypsum, contributed 31% of sulfate, while sulfide oxidation, sewage, and atmospheric deposition contributed 19%, 18%, and 24%, respectively. The tailings pond near Xincha Creek has a higher sulfate release potential than the processing plant near Cha Creek. In the mainstream, sulfide oxidation contributed 25%, primarily from mine drainage. Anthropogenic sources, including sulfide oxidation, fertilizers, and sewage, made up about 50% of the total sulfate, with sulfide oxidation accounting for half of this input. The strong correlation between the Zn and SO42 concentrations (R2 = 0.82) and between the Zn and the contribution from the sulfide oxidation (R2 = 0.67) indicates their co-release during sulfide oxidation, making SO42 a proxy for tracing Zn sources. This study highlights the utility of δ34SSO4 and δ18OSO4 with SIMMR in tracing anthropogenic inputs and underscores the significant impact of mining on river systems and the sulfur cycle. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
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Figure 1
<p>Geological map and sampling points. (<b>a</b>) The location of Daliangzi Zn-Pb mining area; (<b>b</b>) Stratigraphy and structure of the study area; (<b>c</b>) The locations of the mainstream, tributaries, and water samples collected in the Daqiao River Basin. Note: The red points represent mine water, the gray points represent tailings leachate, the orange points represent main stream water samples, and the white points represent tributary water samples.</p>
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<p>Piper diagram of different types of water sample in Daliangzi mining area.</p>
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<p>Correlations between sulfate and (<b>a</b>) Ca<sup>2</sup><sup>+</sup>, (<b>b</b>) Na<sup>+</sup> + K<sup>+</sup>, (<b>c</b>) Cl<sup>−</sup>, (<b>d</b>) <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math>, (<b>e</b>) Mg<sup>2</sup><sup>+</sup>, and (<b>f</b>) TDS.</p>
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<p>Graphs of (<b>a</b>) non-gypsum-source Ca<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math> and (<b>b</b>) Mg<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>HCO</mi> </mrow> <mn>3</mn> <mo>−</mo> </msubsup> </mrow> </semantics></math>; (<b>c</b>) non-carbonate source of Ca<sup>2+</sup> vs. <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>SO</mi> </mrow> <mn>4</mn> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>.</p>
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<p>The relations between (<b>a</b>) δ<sup>34</sup>S<sub>SO4</sub> and δ<sup>18</sup>O<sub>SO4</sub> and (<b>b</b>) δ<sup>34</sup>S<sub>SO4</sub> and δ<sup>18</sup>O<sub>SO4</sub>-1/[SO<sub>4</sub><sup>2</sup><sup>−</sup>].</p>
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<p>The spatial variation of (<b>a</b>) SO<sub>4</sub><sup>2</sup><sup>−</sup>, (<b>b</b>) Cl<sup>−</sup>, (<b>c</b>) δ<sup>34</sup>S, and (<b>d</b>) δ<sup>18</sup>O characteristics.</p>
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<p>(<b>a</b>) Sulfate contributions at tributaries and (<b>b</b>) their correlations with each other.</p>
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<p>(<b>a</b>) Sulfate contributions at mainstream and (<b>b</b>) their correlations with each other.</p>
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<p>Correlations between Zn and (<b>a</b>) SO<sub>4</sub><sup>2</sup><sup>−</sup> and (<b>b</b>) contribution of sulfide oxidation.</p>
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22 pages, 62868 KiB  
Review
The Main Controlling Factors of the Cambrian Ultra-Deep Dolomite Reservoir in the Tarim Basin
by Kehui Zhang, Xuelian You, Tianyi Ma, Jia Wang, Yifen Wu, Yi Lu and Shaoqi Zhang
Minerals 2024, 14(8), 775; https://doi.org/10.3390/min14080775 - 30 Jul 2024
Viewed by 542
Abstract
The genesis of deep-to-ultra-deep dolomite reservoirs in the Tarim Basin is crucial for exploration and development. The Cambrian subsalt dolomite reservoirs in the Tarim Basin are widely distributed, marking significant prospects for ultra-deep reservoir exploration. Based on big data methodologies, this study collects [...] Read more.
The genesis of deep-to-ultra-deep dolomite reservoirs in the Tarim Basin is crucial for exploration and development. The Cambrian subsalt dolomite reservoirs in the Tarim Basin are widely distributed, marking significant prospects for ultra-deep reservoir exploration. Based on big data methodologies, this study collects and analyzes porosity and permeability data of carbonate reservoirs in the western Tarim Basin, specifically targeting the Cambrian deep-oil and gas-reservoir research. Through an examination of the sedimentary evolution and distribution of carbonate–evaporite sequences, and considering sedimentary facies, stratigraphic sediment thickness, fault zone distribution, and source-reservoir assemblages as primary reference factors, the study explores the macro-distribution patterns of porosity and permeability, categorizing three favorable reservoir zones. The controlling factors for the development of Cambrian carbonate reservoirs on the western part of the Tarim Basin are analyzed from the perspectives of sedimentary and diagenetic periods. Factors such as tectonic activity, depositional environment, microbial activity, and pressure dissolution are analyzed to understand the main causes of differences in porosity and permeability distribution. Comprehensive analysis reveals that the porosity and permeability of the Series2 carbonate reservoirs are notably high, with extensive distribution areas, particularly in the Bachu–Tazhong and Keping regions. The geological pattern of “Three Paleo-uplifts and Two Depressions” facilitated the formation of inner-ramp and intra-platform shoals, creating conducive conditions for the emergence of high-porosity reservoirs. The characteristics of reservoir development are predominantly influenced by diagenetic and tectonic activities. The Miaolingian is chiefly affected by diagenesis, featuring high permeability but lower porosity and smaller distribution range; dolomitization, dissolution, and filling processes under a dry and hot paleoclimate significantly contribute to the formation and preservation of reservoir spaces. In the Furongian, the Keping and Bachu areas display elevated porosity and permeability levels, along with substantial sedimentary thickness. The conservation and development of porosity within thick dolomite sequences are mainly governed by high-energy-particulate shallow-shoal sedimentary facies and various dissolution actions during diagenesis, potentially indicating larger reserves. Full article
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<p>Structural unit division of the Tarim (modified from [<a href="#B37-minerals-14-00775" class="html-bibr">37</a>]).</p>
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<p>Generalized stratigraphic column of Tarim [<a href="#B39-minerals-14-00775" class="html-bibr">39</a>,<a href="#B40-minerals-14-00775" class="html-bibr">40</a>].</p>
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<p>Paleogeography of the Cambrian Series2 in the Tarim Basin and the distribution characteristics of porosity and permeability (adapted paleogeography map from [<a href="#B38-minerals-14-00775" class="html-bibr">38</a>]. Porosity and permeability data sources: [<a href="#B14-minerals-14-00775" class="html-bibr">14</a>,<a href="#B21-minerals-14-00775" class="html-bibr">21</a>,<a href="#B22-minerals-14-00775" class="html-bibr">22</a>,<a href="#B28-minerals-14-00775" class="html-bibr">28</a>,<a href="#B40-minerals-14-00775" class="html-bibr">40</a>,<a href="#B41-minerals-14-00775" class="html-bibr">41</a>,<a href="#B44-minerals-14-00775" class="html-bibr">44</a>,<a href="#B45-minerals-14-00775" class="html-bibr">45</a>,<a href="#B48-minerals-14-00775" class="html-bibr">48</a>,<a href="#B49-minerals-14-00775" class="html-bibr">49</a>,<a href="#B50-minerals-14-00775" class="html-bibr">50</a>,<a href="#B51-minerals-14-00775" class="html-bibr">51</a>,<a href="#B52-minerals-14-00775" class="html-bibr">52</a>,<a href="#B53-minerals-14-00775" class="html-bibr">53</a>,<a href="#B54-minerals-14-00775" class="html-bibr">54</a>,<a href="#B55-minerals-14-00775" class="html-bibr">55</a>,<a href="#B56-minerals-14-00775" class="html-bibr">56</a>,<a href="#B57-minerals-14-00775" class="html-bibr">57</a>,<a href="#B58-minerals-14-00775" class="html-bibr">58</a>,<a href="#B59-minerals-14-00775" class="html-bibr">59</a>,<a href="#B60-minerals-14-00775" class="html-bibr">60</a>,<a href="#B61-minerals-14-00775" class="html-bibr">61</a>,<a href="#B62-minerals-14-00775" class="html-bibr">62</a>,<a href="#B63-minerals-14-00775" class="html-bibr">63</a>,<a href="#B64-minerals-14-00775" class="html-bibr">64</a>,<a href="#B65-minerals-14-00775" class="html-bibr">65</a>,<a href="#B66-minerals-14-00775" class="html-bibr">66</a>,<a href="#B67-minerals-14-00775" class="html-bibr">67</a>,<a href="#B68-minerals-14-00775" class="html-bibr">68</a>,<a href="#B69-minerals-14-00775" class="html-bibr">69</a>,<a href="#B70-minerals-14-00775" class="html-bibr">70</a>,<a href="#B71-minerals-14-00775" class="html-bibr">71</a>,<a href="#B72-minerals-14-00775" class="html-bibr">72</a>,<a href="#B73-minerals-14-00775" class="html-bibr">73</a>,<a href="#B74-minerals-14-00775" class="html-bibr">74</a>,<a href="#B75-minerals-14-00775" class="html-bibr">75</a>,<a href="#B76-minerals-14-00775" class="html-bibr">76</a>]).</p>
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<p>Residual thickness of the Cambrian Series2 strata in the Tarim Basin and evaluation of carbonate reservoirs [<a href="#B9-minerals-14-00775" class="html-bibr">9</a>,<a href="#B77-minerals-14-00775" class="html-bibr">77</a>].</p>
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<p>Paleogeography of the Cambrian Miaolingian in the Tarim Basin and the distribution characteristics of porosity and permeability (adapted paleogeography map from [<a href="#B38-minerals-14-00775" class="html-bibr">38</a>]. Porosity and permeability data sources: [<a href="#B21-minerals-14-00775" class="html-bibr">21</a>,<a href="#B22-minerals-14-00775" class="html-bibr">22</a>,<a href="#B28-minerals-14-00775" class="html-bibr">28</a>,<a href="#B40-minerals-14-00775" class="html-bibr">40</a>,<a href="#B46-minerals-14-00775" class="html-bibr">46</a>,<a href="#B51-minerals-14-00775" class="html-bibr">51</a>,<a href="#B52-minerals-14-00775" class="html-bibr">52</a>,<a href="#B53-minerals-14-00775" class="html-bibr">53</a>,<a href="#B57-minerals-14-00775" class="html-bibr">57</a>,<a href="#B59-minerals-14-00775" class="html-bibr">59</a>,<a href="#B63-minerals-14-00775" class="html-bibr">63</a>,<a href="#B65-minerals-14-00775" class="html-bibr">65</a>,<a href="#B68-minerals-14-00775" class="html-bibr">68</a>,<a href="#B71-minerals-14-00775" class="html-bibr">71</a>,<a href="#B73-minerals-14-00775" class="html-bibr">73</a>,<a href="#B74-minerals-14-00775" class="html-bibr">74</a>,<a href="#B82-minerals-14-00775" class="html-bibr">82</a>]).</p>
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<p>Residual thickness of the Cambrian Miaolingian strata in the Tarim Basin and evaluation of carbonate reservoirs [<a href="#B9-minerals-14-00775" class="html-bibr">9</a>,<a href="#B77-minerals-14-00775" class="html-bibr">77</a>,<a href="#B78-minerals-14-00775" class="html-bibr">78</a>].</p>
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<p>Paleogeography of the Cambrian Furongian in the Tarim Basin and the distribution characteristics of porosity and permeability (adapted paleogeography map from [<a href="#B38-minerals-14-00775" class="html-bibr">38</a>]. Porosity and permeability data sources: [<a href="#B21-minerals-14-00775" class="html-bibr">21</a>,<a href="#B51-minerals-14-00775" class="html-bibr">51</a>,<a href="#B53-minerals-14-00775" class="html-bibr">53</a>,<a href="#B68-minerals-14-00775" class="html-bibr">68</a>,<a href="#B73-minerals-14-00775" class="html-bibr">73</a>,<a href="#B74-minerals-14-00775" class="html-bibr">74</a>,<a href="#B82-minerals-14-00775" class="html-bibr">82</a>,<a href="#B83-minerals-14-00775" class="html-bibr">83</a>,<a href="#B86-minerals-14-00775" class="html-bibr">86</a>,<a href="#B87-minerals-14-00775" class="html-bibr">87</a>,<a href="#B88-minerals-14-00775" class="html-bibr">88</a>]).</p>
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<p>Residual thickness of the Cambrian Furongian strata in the Tarim Basin and evaluation of carbonate reservoirs [<a href="#B77-minerals-14-00775" class="html-bibr">77</a>,<a href="#B89-minerals-14-00775" class="html-bibr">89</a>].</p>
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<p>Micrographs of the Keping area; (<b>a</b>) the sandstone of the Yuertusi Formation is mainly developed near the paleo-uplift, including quartz sandstone and siltstone; (<b>b</b>) the Yuertusi Formation siltstone; (<b>c</b>) the finely crystalline dolomite of the lower Qiulitage Formation; (<b>d</b>) medium-crystalline dolomite of the lower Qiulitage Formation.</p>
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25 pages, 6625 KiB  
Article
Fluid Inclusion, Rare Earth Element Geochemistry, and Isotopic (O and S) Characteristics of the Ardakan Barite Deposit, Yazd Province, Iran
by Ebrahim Ansari, Farhad Ehya, Ghodratollah Rostami Paydar and Sara Maleki Kheymehsari
Minerals 2024, 14(8), 739; https://doi.org/10.3390/min14080739 - 24 Jul 2024
Viewed by 608
Abstract
The stratabound barite mineralization occurs in the Ardakan deposit as patches and veins in the dolomites and limestones of the Middle Triassic Shotori Formation. Rare-earth element (REE) geochemistry, O and S isotopes, and fluid inclusion data were used to identify the mode of [...] Read more.
The stratabound barite mineralization occurs in the Ardakan deposit as patches and veins in the dolomites and limestones of the Middle Triassic Shotori Formation. Rare-earth element (REE) geochemistry, O and S isotopes, and fluid inclusion data were used to identify the mode of barite formation. Barite is associated with subordinate fluorite and quartz and, to a lesser extent, with sphalerite, malachite, chrysocolla, and iron and manganese oxide-hydroxides. Barite contains a very low ∑REE concentration (14.80–19.59 ppm) and is enriched in light rare-earth elements (LREEs) relative to heavy rare-earth elements (HREEs). The low ∑REE content and the Ce/La ratio (4.0–6.5) indicate a hydrothermal (terrestrial) origin of the barite. Similar to barite, the ∑REE content in fluorite is low (0.14–6.52 ppm) and suggests a sedimentary setting. The Tb/Ca versus Tb/La diagram also indicates a hydrothermal origin of fluorite. The δ34S values in the barite (+27.9 to +32.4‰) indicate that the sulfur most likely originates from evaporites and/or connate waters from the Late Precambrian to the Lower Cambrian. The δ18O values (+15.9 to +18.1‰) in the barite show that the oxygen originated either from Late Precambrian–Lower Cambrian evaporites or from basinal brines with slightly higher δ18O values than the evaporites. The salinity and homogenization temperature ranges of the aqueous fluid inclusions in barite, fluorite, and quartz (0.88–16.89 wt% NaCl equivalent and 90–270 °C, respectively) reveal that the mineralizing fluids were formed from basinal brines with the participation of heated meteoric water. From this, it is concluded that the Ardakan barite deposit was formed by the meeting of heated, ascending sulfate-bearing meteoric water and cooler, Ba-bearing connate water trapped in the overlying Middle Triassic dolomites and limestones. The Ardakan deposit belongs to the structure-related class and the unconformity-related subclass of barite deposits. Full article
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<p>A map of the main tectonic zones of Iran with the location of the Ardakan barite deposit (modified after [<a href="#B33-minerals-14-00739" class="html-bibr">33</a>]).</p>
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<p>Simplified geologic map of the Ardakan area (modified after [<a href="#B29-minerals-14-00739" class="html-bibr">29</a>]).</p>
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<p>(<b>a</b>,<b>b</b>) Photographs showing barite veins and patches in the carbonate rocks of the Shotori Formation. (<b>c</b>) Photograph showing calcite veins in the carbonate host rocks with a zebra structure. (<b>d</b>) Photograph showing a thrust fault in the carbonate host rocks. (<b>e</b>) Photograph showing fractures in the carbonate host rocks filled with iron oxides and hydroxides.</p>
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<p>(<b>a</b>) Photomicrograph showing a dolosparite host rock with the spaces between dolomite crystals filled by barite. (<b>b</b>) Photomicrograph showing a dolomitized limestone with local coarse dolomite and quartz crystals. (<b>c</b>) Photomicrograph showing a mudstone containing coarse veinlets of sparry calcite and quartz crystals. (<b>d</b>) Photomicrograph showing a mudstone with small tubular barite and quartz crystals. (<b>e</b>) Photomicrograph showing a calcarenite containing small patches of iron oxides and hydroxides. (<b>f</b>,<b>g</b>) Photomicrographs showing large tubular barite crystals with quartz, calcite, and dolomite filling the spaces between barite crystals. (<b>h</b>) Photomicrograph showing small tubular barite crystals. (<b>i</b>) Photomicrograph showing the first (Brt 1) and the second generations (Brt 2) of barite crystals; and (<b>j</b>) equal-dimension barite crystals forming a granular texture. All photomicrographs in XPL. Brt = barite, Dol = dolomite, Cal = calcite, and Qz = quartz.</p>
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<p>(<b>a</b>) Photograph showing a pure crystalline patch of barite. (<b>b</b>) Photograph showing a crystalline patch of barite with perfect rhombic cleavage. (<b>c</b>) Photograph showing a veiny barite mineralization at the centimeter scale. (<b>d</b>) Photograph showing brecciated carbonate host rocks cemented by barite. (<b>e</b>) Photograph showing barite with fluorite and sphalerite. (<b>f</b>) Photograph showing barite with fluorite and malachite. (<b>g</b>) Photograph showing quartz and iron oxides and hydroxides. (<b>h</b>) Photograph showing barite with iron oxides and hydroxides, malachite, and fluorite. (<b>i</b>) Photograph showing barite with chrysocolla. (<b>j</b>) Photograph showing barite with manganese oxides and chrysocolla.</p>
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<p>Paragenetic sequence of the ore from the Ardakan deposit.</p>
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<p>Chondrite-normalized [<a href="#B36-minerals-14-00739" class="html-bibr">36</a>] REE patterns for carbonate host rocks from the Ardakan deposit.</p>
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<p>(<b>a</b>) Secondary fluid inclusions with a linear trend in fluorite. (<b>b</b>) Liquid-rich biphase fluid inclusion (L+V) with irregular shape in barite. (<b>c</b>) Liquid-rich biphase fluid inclusion (L+V) with negative crystal shape in fluorite. (<b>d</b>) Irregularly shaped biphase liquid-rich fluid inclusions (L+V) in quartz.</p>
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<p>(<b>a</b>) Frequency histogram of salinity for fluid inclusions in barite, fluorite, and quartz samples. (<b>b</b>) Frequency histogram of homogenization temperatures for fluid inclusions in barite, fluorite, and quartz samples.</p>
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<p>Tb/Ca versus Tb/La diagram for the fluorite samples from the Ardakan deposit (after [<a href="#B46-minerals-14-00739" class="html-bibr">46</a>,<a href="#B47-minerals-14-00739" class="html-bibr">47</a>]).</p>
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<p>The δ<sup>34</sup>S and δ<sup>18</sup>O values of barite samples from the Ardakan deposit compared to the δ<sup>34</sup>S (<b>a</b>) and δ<sup>18</sup>O (<b>b</b>) curves of seawater [<a href="#B49-minerals-14-00739" class="html-bibr">49</a>]. The heavy lines are the best estimate of δ<sup>34</sup>S and δ<sup>18</sup>O of the seawater. The shaded areas are the uncertainty related to the curves.</p>
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<p>The T<span class="html-italic"><sub>h</sub></span>–salinity diagram for fluid inclusions in barite, fluorite, and quartz from the Ardakan deposit.</p>
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20 pages, 9130 KiB  
Article
Constraining Geogenic Sources of Boron Impacting Groundwater and Wells in the Newark Basin, USA
by Larbi Rddad and Steven Spayd
Hydrology 2024, 11(7), 107; https://doi.org/10.3390/hydrology11070107 - 21 Jul 2024
Cited by 1 | Viewed by 1223
Abstract
The Newark Basin comprises Late Triassic and Early Jurassic fluvio-lacustrine rocks (Stockton, Lockatong, Passaic, Feltville, Towaco, and Boonton Formations) and Early Jurassic diabase intrusions and basalt lava flows. Boron concentrations in private well water samples range up to 18,000 μg/L, exceeding the U.S. [...] Read more.
The Newark Basin comprises Late Triassic and Early Jurassic fluvio-lacustrine rocks (Stockton, Lockatong, Passaic, Feltville, Towaco, and Boonton Formations) and Early Jurassic diabase intrusions and basalt lava flows. Boron concentrations in private well water samples range up to 18,000 μg/L, exceeding the U.S. Environmental Protection Agency Health Advisory of 2000 μg/L for children and 5000 μg/L for adults. Boron was analyzed in minerals, rocks, and water samples using FUS-ICPMS, LA-ICP-MS, and MC ICP-MS, respectively. Boron concentrations reach up to 121 ppm in sandstone of the Passaic Formation, 42 ppm in black shale of the Lockatong Formation, 31.2 ppm in sandstone of the Stockton Formation, and 36 ppm in diabase. The δ11B isotopic values of groundwater range from 16.7 to 32.7‰, which fall within those of the diabase intrusion (25 to 31‰). Geostatistical analysis using Principal Component Analysis (PCA) reveals that boron is associated with clay minerals in black shales and with Na-bearing minerals (possibly feldspar and evaporite minerals) in sandstones. The PCA also shows that boron is not associated with any major phases in diabase intrusion, and is likely remobilized from the surrounding rocks by the intrusion-related late hydrothermal fluids and subsequently incorporated into diabase. Calcite veins found within the Triassic rock formations exhibit relatively elevated concentrations ranging from 6.3 to 97.3 ppm and may contain micro-inclusions rich in boron. Based on the available data, it is suggested that the primary sources of boron contaminating groundwater in the area are clay minerals in black shales, Na-bearing minerals in sandstone, diabase intrusion-related hydrothermal fluids, and a contribution from calcite veins. Full article
(This article belongs to the Special Issue Isotope Hydrology in the U.S.)
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<p>Pangea during the middle Norian showing the Early Mesozoic rifting zone (shaded) and the preserved basins of the Newark Supergroup, including the Newark basin (green) (<b>A</b>). Early Mesozoic rift basins of eastern North America (<b>B</b>) (edited by Rddad [<a href="#B9-hydrology-11-00107" class="html-bibr">9</a>] after Olsen et al. [<a href="#B10-hydrology-11-00107" class="html-bibr">10</a>]). For interpretation of the colors in this figure’s legend, the reader is referred to the web version of this article.</p>
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<p>Location of A–B cross-section (<b>A</b>) and a geologic cross-section A-B in the Newark Basin showing the Triassic formations and pre-Triassic basement (<b>B</b>) (edited by Rddad et al. [<a href="#B12-hydrology-11-00107" class="html-bibr">12</a>] after Olsen et al. [<a href="#B10-hydrology-11-00107" class="html-bibr">10</a>]).</p>
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<p>Selected hand-specimen samples of calcite veins hosted in the Late Triassic fluvio-lacustrine formations of Stockton (<b>A</b>), Lockatong (<b>B</b>), and Passaic (<b>C</b>), along with those of sulfides (Py = Pyrite, Cpy = Chalcopyrite) and calcite (Ca) (<b>D</b>) associated with the Early Jurassic diabase. The sulfides, calcite, and diabase samples were collected at the Moore station quarry (<b>E</b>).</p>
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<p>PCs biplot with loading values for 25 major and trace elements in black shale and limestone, Newark Basin.</p>
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<p>PCs biplot with loading values for 25 major and trace elements in sandstone, Newark Basin.</p>
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<p>PCs biplot with loading values for 25 major and trace elements in diabase, Newark Basin.</p>
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<p>Map showing boron distribution in water in the studied area, Newark Basin (after Spayd [<a href="#B6-hydrology-11-00107" class="html-bibr">6</a>]).</p>
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<p>Schematic model showing the three stages of boron distribution: Late Triassic phase involves fixation of boron in Na-bearing minerals. (<b>A</b>) Early Jurassic phase entails the emplacement of (i) the diabase intrusion and remobilization of boron from the surrounding rocks, subsequently incorporating it into the diabase and (ii) the formation of the calcite veins. (<b>B</b>) Current time phase includes groundwater flow through available open spaces (WBFs), leaching boron from boron-rich phases (<b>C</b>).</p>
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28 pages, 8168 KiB  
Article
Subsurface Hydrodynamics of the Southeastern Taoudéni Basin (West Africa) through Hydrogeochemistry and Isotopy
by Succès Malundama Kutangila, Moussa Bruno Kafando, Amadou Keita, Lawani Adjadi Mounirou, Roland Yonaba, Mahamadi Ouedraogo and Mahamadou Koita
Water 2024, 16(13), 1922; https://doi.org/10.3390/w16131922 - 5 Jul 2024
Viewed by 971
Abstract
The Taoudéni Basin, spanning 20% of Burkina Faso, holds vital aquifers for the Sahel’s water security and development. However, limited understanding of these aquifers’ hydrodynamics, including the flow patterns, mineralization processes, and renewal rates, hinders sustainable management practices in this arid region. Therefore, [...] Read more.
The Taoudéni Basin, spanning 20% of Burkina Faso, holds vital aquifers for the Sahel’s water security and development. However, limited understanding of these aquifers’ hydrodynamics, including the flow patterns, mineralization processes, and renewal rates, hinders sustainable management practices in this arid region. Therefore, this study aims to investigate the aquifer hydrodynamics, mineralization processes and groundwater renewal in the transboundary Taoudéni Basin. Through a combination of hydrogeochemical and isotopic analyses, alongside existing data, this study examines 347 physicochemical samples, 149 stable isotope samples, and 71 tritium samples collected from 2013 to 2022. The findings reveal mineralization and stable isotopes (δ18O, δ2H) spatially aligned with the groundwater flow direction, validating this and indicating potentially multiple independent aquifers. The predominant mineralization mechanisms involve silicate hydrolysis and carbonate dissolution, supplemented by minor processes like evaporitic dissolution and cation exchange. The anthropogenic influence suggests potential groundwater recharge with potential pollution in the “SAC1”, “SAC2”, “GFR”, “GGQ”, and “GKS” geological formations. The stable isotopes (δ18O, δ2H) indicate recharge occurred over 4.5 kyr B.P., while tritium (3H) analysis confirms the presence of old, mixed waters, indicating slow renewal. Overall, this study highlights the minimal recent recharge and limited renewal rates, questions tritium’s efficacy for old water detection, and emphasizes the need for sustainable management. Full article
(This article belongs to the Section Hydrogeology)
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<p>Southeastern part of the Taoudéni Basin: (<b>a</b>) location map and climatic zones (data from [<a href="#B29-water-16-01922" class="html-bibr">29</a>]); (<b>b</b>) hydrography and watersheds; (<b>c</b>) and the topography.</p>
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<p>Stratigraphy of the southeastern margin of the Taoudéni Basin. GI: lower sandstones, GKS: the Kawara Sindou sandstones, GFG: the fine-grained sandstones with glauconite, GGQ: the quartz granulate sandstones, SAC1: the siltstones, argillites and carbonates of Guena-Souroukoundinga, GFR: the pink fine-grained sandstones, SAC2: the siltstones, argilites and carbonates of Samandeni-Kiebani, SQ: the siltstones and quartzite of the Fo Pass, GFB: the Fo-Badiangara sandstone, CT: Continental Terminal (adapted from [<a href="#B23-water-16-01922" class="html-bibr">23</a>,<a href="#B29-water-16-01922" class="html-bibr">29</a>]).</p>
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<p>(<b>a</b>) Detailed geology of the southeastern margin of the Taoudéni Basin (data from [<a href="#B34-water-16-01922" class="html-bibr">34</a>]) and (<b>b</b>) sampling map. GFG: the fine-grained sandstones with glauconite, GGQ: the quartz granulate sandstones, SAC1: the siltstones, argillites and carbonates of Guena-Souroukoundinga, GFR: the pink fine-grained sandstones, SAC2: the siltstones, argilites and carbonates of Samandeni-Kiebani, SQ: the siltstones and quartzite of the Fo Pass, GFB: the Fo-Badiangara sandstone, CT: Continental Terminal, DOL: dolerite.</p>
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<p>Physico-chemical parameters by geological formation: (<b>a</b>) temperature, (<b>b</b>) pH, (<b>c</b>) total alkalinity, and (<b>d</b>) electrical conductivity.</p>
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<p>Spatial distribution map of (<b>a</b>) piezometric head map (data from [<a href="#B6-water-16-01922" class="html-bibr">6</a>,<a href="#B29-water-16-01922" class="html-bibr">29</a>]); (<b>b</b>) electrical conductivities; (<b>c</b>) <sup>2</sup>H; and (<b>d</b>) <sup>18</sup>O with geological limits.</p>
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<p>Profile of isotopic contents and electrical conductivity according to the flow direction (red arrow in <a href="#water-16-01922-f005" class="html-fig">Figure 5</a>).</p>
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<p>Descriptive statistics on the major elements’ concentration by lithology sampled: (<b>a</b>) calcium, (<b>b</b>) magnesium, (<b>c</b>) sodium, (<b>d</b>) potassium, (<b>e</b>) bicarbonates, (<b>f</b>) chlorides, (<b>g</b>) sulfates, and (<b>h</b>) nitrates (outliers have been removed to improve the scale).</p>
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<p>Wilcox diagram: water quality for irrigation taking sodium into account.</p>
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<p>Piper diagram for water from geological formations: (<b>a</b>) sandstone IC, and (<b>b</b>) clayey-carbonate IC and (<b>c</b>) CT andsurface water.</p>
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<p>Groundwater stable isotopes and tritium statistics: (<b>a</b>) deuterium, (<b>b</b>) oxygen-18, (<b>c</b>) deuterium excess, and (<b>d</b>) tritium.</p>
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<p>Representation of the relationship between oxygen-18 and deuterium for groundwater and surface water.</p>
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<p>Binary diagrams of waters hosted in Infracambrian sandstone rocks (sandstones IC): (<b>a</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>d</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mo> </mo> <msup> <mrow> <mi>N</mi> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>N</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Binary diagrams of water in clay-carbonate formations (clayey-carbonate IC): (<b>a</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> <mo>,</mo> </mrow> </semantics></math> (<b>d</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mo> </mo> <msup> <mrow> <mi>N</mi> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>N</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Binary diagrams of water in Continental Terminal formations (CT): (<b>a</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>C</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>S</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> <mo>,</mo> </mrow> </semantics></math> (<b>d</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> </mrow> </semantics></math>, (<b>e</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mo> </mo> <msup> <mrow> <mi>N</mi> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>, (<b>f</b>) <math display="inline"><semantics> <mrow> <mo> </mo> <mi>H</mi> <mi>C</mi> <msubsup> <mrow> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mi>N</mi> <msup> <mrow> <mi>a</mi> </mrow> <mrow> <mo>+</mo> </mrow> </msup> </mrow> </semantics></math>.</p>
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<p>Mineral saturation indices according to the different reservoirs presented in values in the boxplot (<b>left</b>) and in the point cloud (<b>right</b>).</p>
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<p>Binary diagram <math display="inline"><semantics> <mrow> <mfenced open="[" close="]" separators="|"> <mrow> <msup> <mrow> <mi>C</mi> <mi>a</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> <mo>+</mo> <msup> <mrow> <mi>M</mi> <mi>g</mi> </mrow> <mrow> <mn>2</mn> <mo>+</mo> </mrow> </msup> </mrow> </mfenced> <mo> </mo> <mi mathvariant="normal">v</mi> <mi mathvariant="normal">s</mi> <mo>.</mo> <mo> </mo> <mfenced open="[" close="]" separators="|"> <mrow> <msubsup> <mrow> <mi>H</mi> <mi>C</mi> <mi>O</mi> </mrow> <mrow> <mn>3</mn> </mrow> <mrow> <mo>−</mo> </mrow> </msubsup> <mo>+</mo> <msubsup> <mrow> <mi>S</mi> <mi>O</mi> </mrow> <mrow> <mn>4</mn> </mrow> <mrow> <mn>2</mn> <mo>−</mo> </mrow> </msubsup> </mrow> </mfenced> </mrow> </semantics></math> for the IC sandstone reservoir (<b>a</b>), IC clay-carbonate reservoir (<b>b</b>) and Continental Terminal reservoir (<b>c</b>).</p>
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<p>Representation of the relationship between oxygen-18 and deuterium for the groundwater of different lithologies and surface water.</p>
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<p>Spatialization of tritium content from 2013 to 2022 (relative age of groundwater).</p>
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33 pages, 11825 KiB  
Article
Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea
by Osama Alrabayah, Danu Caus, Robert Alban Watson, Hanna Z. Schulten, Tobias Weigel, Lars Rüpke and Djamil Al-Halbouni
Remote Sens. 2024, 16(13), 2264; https://doi.org/10.3390/rs16132264 - 21 Jun 2024
Cited by 1 | Viewed by 935
Abstract
Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an [...] Read more.
Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data and manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed to improve sinkhole mapping using accessible satellite imagery, which could enhance management practices related to sinkholes and other geohazards in evaporite karst regions. The developed AI system is centered around the U-Net architecture. The model was initially trained on a high-resolution drone dataset (0.1 m GSD, phase I), covering 250 sinkhole instances. Subsequently, it was additionally fine-tuned on a larger dataset from a Pleiades Neo satellite image (0.3 m GSD, phase II) with 1038 instances. The training process involved an automated image-processing workflow and strategic layer freezing and unfreezing to adapt the model to different input scales and resolutions. We show the usefulness of initial layer features learned on drone data, for the coarser, more readily-available satellite inputs. The validation revealed high detection accuracy for sinkholes, with phase I achieving a recall of 96.79% and an F1 score of 97.08%, and phase II reaching a recall of 92.06% and an F1 score of 91.23%. These results confirm the model’s accuracy and its capability to maintain high performance across varying resolutions. Our findings highlight the potential of using RGB visual bands for sinkhole detection across different karst environments. This approach provides a scalable, cost-effective solution for continuous mapping, monitoring, and risk mitigation related to sinkhole hazards. The developed system is not limited only to sinkholes however, and can be naturally extended to other geohazards as well. Moreover, since it currently uses U-Net as a backbone, the system can be extended to incorporate super-resolution techniques, leveraging U-Net based latent diffusion models to address the smaller-scale, ambiguous geo-structures that are often found in geoscientific data. Full article
(This article belongs to the Special Issue Artificial Intelligence for Natural Hazards (AI4NH))
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Graphical abstract

Graphical abstract
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<p>Schematic representations of different ‘top-down’ algorithmic methods of delineating sinkholes. (<b>A</b>) method of O’Callaghan and Mark, [<a href="#B21-remotesensing-16-02264" class="html-bibr">21</a>], which maps the depressions according to simulated stratification of water within them. Adapted with permission from [<a href="#B21-remotesensing-16-02264" class="html-bibr">21</a>], 2024, Elsevier. (<b>B</b>) the ‘D8’ method of Jenson and Domingue [<a href="#B22-remotesensing-16-02264" class="html-bibr">22</a>], which uses a moving window to map the watersheds within the depression. This method and that shown in (<b>A</b>) become very computationally intensive with high-resolution data. (<b>C</b>) the ‘priority fill’ method of Wang and Liu, [<a href="#B23-remotesensing-16-02264" class="html-bibr">23</a>], which is able to simulate filling of the entire compound depression in one pass of processing. This method offers an improvement in run-time of a factor of 30 on (<b>B</b>), but is not able to capture the internal complexity of the compound depression. Adapted with permission from [<a href="#B23-remotesensing-16-02264" class="html-bibr">23</a>], 2024, Taylor &amp; Francis. (<b>D</b>) The ‘contour tree’ method developed by Wu et al. [<a href="#B24-remotesensing-16-02264" class="html-bibr">24</a>,<a href="#B25-remotesensing-16-02264" class="html-bibr">25</a>], which builds on the ‘priority fill’ method to produce a graph (‘tree’) of contours within the compound depression, allowing nested depressions to be identified and labelled by their rank. This allows for more accurate automated updating of depression location and morphometric databases. The method has since been further refined for efficient computation (see [<a href="#B26-remotesensing-16-02264" class="html-bibr">26</a>,<a href="#B27-remotesensing-16-02264" class="html-bibr">27</a>]). Adapted with permission from [<a href="#B25-remotesensing-16-02264" class="html-bibr">25</a>], 2024, Elsevier.</p>
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<p>Overview of the study area. (<b>A</b>) ESRI satellite imagery of the Dead Sea. The location of part (<b>B</b>) is marked. (<b>B</b>) Pleiades 1-A satellite image from April 2018 of the Ghor Al-Haditha study area on the Dead Sea’s eastern shore. The outline of data collected in the December 2016 drone survey, the extent of sinkhole formation across the study area and the position of the Dead Sea shoreline in 1967 are shown. Additionally, the areas covered by the datasets used for Phase I (Red) and Phase II (Grey) of our study are shown, as are the locations of parts (<b>C</b>,<b>D</b>), which depict sinkholes in both alluvium and mud materials as they appear in the 2016 structure-from-motion orthophoto and in Pleiades Neo satellite imagery from August 2022, respectively. Several new sinkholes have formed in 2022 as compared to 2016, and others have changed in shape and size.</p>
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<p>Relevant computer vision problems within which we could frame our task. We chose image segmentation in the end. (<b>A</b>) <span class="html-italic">Image classification</span>: an entire image is classified according to a label. (<b>B</b>) <span class="html-italic">Object detection</span>: the task of detecting instances of objects of a certain class within an image. (<b>C</b>) <span class="html-italic">Semantic segmentation:</span> label each pixel of an image with a corresponding class, i.e., per pixel classification (<b>D</b>) <span class="html-italic">Instance segmentation:</span> label each pixel of an image with a corresponding class and detect instances of objects of each class within an image.</p>
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<p>The different layers that were used to guide the annotation process for the training dataset in Phase I. The sinkhole cluster shown here is the same as that highlighted in <a href="#remotesensing-16-02264-f005" class="html-fig">Figure 5</a>C–F. (<b>A</b>) RGB orthophoto mosaic. (<b>B</b>) DSM data visualized as a hill-shaded relief map. Contour lines generated from the DSM data with an interval of 1 m were also used. (<b>C</b>) Elevation profile generated within ArcGIS Pro (V. 2.9) along the axis of a sinkhole cluster. The tool was used in special cases to find the exact edges, especially the edges between compound (merged) sinkholes, as presented in the image.</p>
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<p>Generating the sinkhole instance segmentation mask image. (<b>A</b>) The selected area from the drone image for training sample generation. (<b>B</b>) Several depicted sinkholes. Note the 3 compound sinkhole instances. (<b>C</b>) Using different layers to guide the annotation process. (<b>D</b>) Different polygons were manually drawn for each sinkhole instance with precise edges. (<b>E</b>) Converted polygons to a raster layer where each sinkhole is presented using a different colour. (<b>F</b>) TIFF mask image with all the sinkholes in the selected area.</p>
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<p>(<b>A</b>) Defining features of sinkhole outlines. (<b>B</b>) Mapping of sinkholes in the area.</p>
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<p>(<b>A</b>) Drone RGB image for the research area, (<b>B</b>) Sinkhole instance segmentation label image as created for the drone image case, and (<b>C</b>) The derived 3-class label image.</p>
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<p>Overview of the sinkhole instance segmentation pipeline used in phase I of the study. This diagram illustrates the multi-stage process used to train a multi-class U-Net model, adapted from Ronneberger et al. [<a href="#B54-remotesensing-16-02264" class="html-bibr">54</a>]. The workflow begins with pre-processing the mask image (STEP 1) to detect edges between sinkholes, transforming the original two-class mask (Background and Sinkhole) into a three-class mask (Background, Sinkhole, and Edge Class). The input RGB orthophoto and the generated three-class mask are then used to train the multi-class U-Net model (STEP 2). The best-trained model is then applied to segment the full orthophoto, generating a semantically segmented mask (STEP 3). This mask undergoes a post-processing step (STEP 4) to generate the final instance segmentation mask image.</p>
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<p>Model performance for Phase I as judged by the average dice score.</p>
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17 pages, 10624 KiB  
Article
Application of the Data-Driven Method and Hydrochemistry Analysis to Predict Groundwater Level Change Induced by the Mining Activities: A Case Study of Yili Coalfield in Xinjiang, Norwest China
by Ankun Luo, Shuning Dong, Hao Wang, Haidong Cao, Tiantian Wang, Xiaoyu Hu, Chenyu Wang, Shouchuan Zhang and Shen Qu
Water 2024, 16(11), 1611; https://doi.org/10.3390/w16111611 - 5 Jun 2024
Viewed by 763
Abstract
As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes [...] Read more.
As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities. Full article
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Figure 1

Figure 1
<p>The geographic location and hydrogeological profile of the study area.</p>
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<p>The monthly monitored value of phreatic groundwater level elevation (PGL), confined groundwater level elevation (CGL), precipitation (P), water inflow (WI), and unit goal area (GA) during the interval of 2018~2023.</p>
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<p>A schematic flowchart of LSTM algorithm.</p>
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<p>The Gibbs diagram of groundwater samples.</p>
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<p>Relationships between hydrochemical parameters: (<b>a</b>) SI<sub>gypsum</sub> vs. SI<sub>halite</sub>; (<b>b</b>) SI<sub>calcite</sub> vs. SI<sub>dolomite</sub>; (<b>c</b>) Na<sup>+</sup> vs. Cl<sup>−</sup>; (<b>d</b>) Ca<sup>2+</sup> vs. SO<sub>4</sub><sup>2−</sup>; (<b>e</b>) CAI 1 vs. CAI2; (<b>f</b>) (Na<sup>+</sup> + K<sup>+</sup> − Cl<sup>−</sup>) vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>) − (SO<sub>4</sub><sup>2−</sup> + HCO<sub>3</sub><sup>−</sup>); (<b>g</b>) (Ca<sup>2+</sup> + Mg<sup>2+</sup>) vs. (SO<sub>4</sub><sup>2−</sup> + HCO<sub>3</sub><sup>−</sup>); (<b>h</b>) Ca<sup>2+</sup> vs. HCO<sub>3</sub><sup>−</sup>.</p>
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<p>The Piper diagram of groundwater samples collected from phreatic and confined aquifer before and after mining activities.</p>
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<p>Wavelet coherence during the period of 2018~2023 between groundwater level in phreatic aquifer and (<b>a</b>) precipitation, (<b>b</b>) mine water inflow, and (<b>c</b>) unit goal area.</p>
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<p>Wavelet coherence during the period of 2018~2023 between groundwater level in confined and. (<b>a</b>) precipitation, (<b>b</b>) mine water inflow, and (<b>c</b>) unit goal area.</p>
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<p>The training and prediction results of PGL model using. (<b>a</b>) Decision Tree algorithm, (<b>b</b>) LSTM algorithm.</p>
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<p>The training and prediction results of CGL model using. (<b>a</b>) Decision Tree algorithm, (<b>b</b>) LSTM algorithm.</p>
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<p>Scatter plot of the monitored value vs. the simulated value in the training and prediction stage of PGL model. (<b>A</b>) and (<b>a</b>) represent the training and prediction stage using Decision Tree, respectively. (<b>B</b>) and (<b>b</b>) represent the training and prediction stage using LSTM, respectively.</p>
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<p>Scatter plot of the monitored value vs. the simulated value in the training and prediction stage of CGL model. (<b>A</b>) and (<b>a</b>) represent the training and prediction stage using Decision Tree, respectively. (<b>B</b>) and (<b>b</b>) represent the training and prediction stage using LSTM, respectively.</p>
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23 pages, 4462 KiB  
Article
Synergic Origin and Evolution of TDS, Mg and Fluoride in Groundwater as Relative to Chronic Kidney Disease of Unknown Etiology (CKDu) in Sri Lanka
by K. S. G. S. Priyadarshanee, Zhonghe Pang, E. A. N. V. Edirisinghe, H. M. T. G. A. Pitawala, J. D. C. Gunasekara, W. M. G. S. Wijesooriya, Yinlei Hao, Yifan Bao and Jiao Tian
Water 2024, 16(11), 1606; https://doi.org/10.3390/w16111606 - 4 Jun 2024
Cited by 1 | Viewed by 785
Abstract
The rural population in the Dry Zone of Sri Lanka is largely affected by Chronic Kidney Disease of Unknown etiology (CKDu). According to the multidisciplinary research carried out so far, quality of groundwater is considered one of the possible causative factors for CKDu. [...] Read more.
The rural population in the Dry Zone of Sri Lanka is largely affected by Chronic Kidney Disease of Unknown etiology (CKDu). According to the multidisciplinary research carried out so far, quality of groundwater is considered one of the possible causative factors for CKDu. Therefore, assessment of the quality of groundwater being used for drinking and its evolution mechanism is the key to identifying the linkage between CKDu and drinking water. This study aimed to perform a detailed investigation on groundwater sources using isotopic, chemical, and hydrogeological methods in the CKDu-endemic (site A) and the control area (sedimentary formation—site B) in the Malwathu Oya basin and the control areas in the Malala Oya basin (site C) selected for a systematic comparison. Our investigation shows that elevated levels of TDS, magnesium, and fluoride in the shallow groundwater affected by climatic, geochemical, and hydrogeological processes may contribute to the CKDu in the Dry Zone of Sri Lanka. All the groundwater samples analysed have exceeded the hardness threshold. Prominent Mg hardness proportion together with excess F in the CKDu endemic area may produce nephrotoxic MgF2 complexes that may trigger renal damage. In contrast, NaF complexes in the CKDu control area leads to reduction of F toxicity in the human body. Elevated F and Mg2+ are found in site A, low F and high Mg2+ in site B, and either combinations of low F and low Mg2+, high F and low Mg2+, or low F with high Mg2+ in site C. TDS, hardness, Mg2+, Na+, and F are formed with different mechanisms in the three selected areas. The primary process that regulates the evolution of groundwater types and contents in sites A and C is the weathering of silicates. Similarly, in site A, carbonate dissolution and reverse ion exchange are quite strong. Cation exchange and evaporite dissolution are more pronounced in site C. Shallow groundwaters are evapo-concentrated, hence their quality deteriorates more significantly than the deep groundwater in the CKDu endemic area. Dilution decreases the ion content in site A while evaporite dissolution increases it in site C after the rainy season. Evaporation and seawater mixing affect the quality of groundwater in site B. It is also found that a statistically significant difference exists in the F/Na+, F/Mg2+, and F/Ca2+ between the endemic and control areas. Intensive rock weathering combined with desorption has added excess F to the groundwater in site A, while cation exchange and fluorite dissolution are contributing factors in site C. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
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<p>(<b>a</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malwathu Oya basin (site A and site B); (<b>b</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malala Oya basin (site C).</p>
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<p>(<b>a</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malwathu Oya basin (site A and site B); (<b>b</b>): A schematic hydrogeological cross section and spatial distribution of sampling locations in the Malala Oya basin (site C).</p>
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<p>The plot showing the F<sup>−</sup> and Mg<sup>2+</sup> variation of groundwater during the dry season.</p>
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<p>A Piper trilinear plot illustrating the hydro-geochemical facies of groundwater during the wet and dry seasons.</p>
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<p>Gibb’s plot illustrating the hydro-chemical facies of groundwater during the wet and dry season.</p>
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<p>Major ion relationships of (<b>a</b>) Cl<sup>−</sup> vs. Na<sup>+</sup> (meq/L) (<b>b</b>) Mg<sup>2+</sup> vs. Ca<sup>2+</sup> (meq/L) (<b>c</b>) HCO<sub>3</sub><sup>−</sup> vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>) (meq/L) (<b>d</b>) Mg<sup>2+</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup> (<b>e</b>) HCO<sub>3</sub><sup>−</sup>/Na<sup>+</sup> vs. Ca<sup>2+</sup>/Na<sup>+</sup> and (<b>f</b>) (Na<sup>+</sup>/K<sup>+</sup>)-Cl<sup>−</sup> vs. (Ca<sup>2+</sup> + Mg<sup>2+</sup>)-(HCO<sub>3</sub><sup>−</sup> + SO<sub>4</sub><sup>2−</sup>) of groundwater in the CKDu endemic and control area.</p>
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<p>δ<sup>2</sup>H vs. δ<sup>18</sup>O relationship of groundwater in (<b>a</b>,<b>b</b>). CKDu endemic Malawthu Oya (<b>d</b>,<b>e</b>). CKDu control Malala Oya, and (<b>c</b>,<b>f</b>) relationship of D-excess and TDS in both dry and wet seasons.</p>
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<p>Pearson’s correlation analysis among water quality parameters of groundwater in CKDu endemic (<b>a</b>,<b>c</b>) and control areas (<b>b</b>,<b>d</b>).</p>
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20 pages, 9443 KiB  
Article
Hydrogeochemical Characterization of an Intermontane Aquifer Contaminated with Arsenic and Fluoride via Clustering Analysis
by José Rafael Irigoyen-Campuzano, Diana Barraza-Barraza, Mélida Gutiérrez, Luis Arturo Torres-Castañón, Liliana Reynoso-Cuevas and María Teresa Alarcón-Herrera
Hydrology 2024, 11(6), 76; https://doi.org/10.3390/hydrology11060076 - 31 May 2024
Viewed by 797
Abstract
The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na+, K+, Ca2+, Mg2+, F, Cl [...] Read more.
The controlling hydrogeochemical processes of an intermontane aquifer in central Mexico were identified through multivariate statistical analysis. Hierarchical cluster (HCA) and k-means clustering analyses were applied to Na+, K+, Ca2+, Mg2+, F, Cl, SO42−, NO3, HCO3, As, pH and electrical conductivity in 40 groundwater samples collected from shallow and deep wells, where As and F are contaminants of concern. The effectiveness of each hierarchical and k-means clustering method in explaining solute concentrations within the aquifer and the co-occurrence of arsenic and fluoride was tested by comparing two datasets containing samples from 40 and 36 wells, the former including ionic balance outliers (>10%). When tested without outliers, cluster quality improved by about 5.4% for k-means and 7.3% for HCA, suggesting that HCA is more sensitive to ionic balance outliers. Both algorithms yielded similar clustering solutions in the outlier-free dataset, aligning with the k-means solution for all 40 samples, indicating that k-means was the more robust of the two methods. k-means clustering resolved fluoride and arsenic concentrations into four clusters (K1 to K4) based on variations in Na+, Ca2+, As, and F. Cluster K2 was a Na-HCO3 water type with high concentrations of As and F. Clusters K1, K3, and K4 exhibited a Ca-HCO3, Na-Ca-HCO3, and Ca-Na-HCO3 water types, respectively, with decreasing As and F concentrations following the order K2 > K3 > K1 > K4. The weathering of evaporites and silicates and Na-Ca ion exchange with clays were the main processes controlling groundwater geochemistry. The dissolution of felsic rocks present in the aquifer fill is a likely source of As and F, with evaporation acting as an important concentration factor. Full article
(This article belongs to the Topic Advances in Hydrogeological Research)
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<p>Study area, the main exploitation zone, in the eastern part of the Valle del Guadiana aquifer.</p>
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<p>Piper diagram of the 40 sampled sites. Red points were omitted in 30-sample subset.</p>
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<p>Principal component analysis validation for grouping tendency: (<b>a</b>) four groups were identified in the original 40-well dataset; (<b>b</b>) three groups were identified in the depurated 34-well dataset.</p>
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<p>Dendrogram of sampling sites constructed using Ward’s method and Euclidean distances.</p>
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<p>Clusters of sampling sites constructed using the k-means clustering algorithm. Concentration is expressed as the median value of the group.</p>
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<p>Piper diagram color-coded by cluster. (<b>left</b>): clusters from k-means algorithm; (<b>right</b>): clusters from hierarchical clustering algorithm. (Note 1: Cluster boundaries are arbitrarily drawn to highlight the difference in the clusterization pattern. Note 2: Group colors in this figure were assigned for clusters visualization).</p>
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<p>Comparative Stiff diagrams of the groups formed by the two algorithms tested with the depurated 34 well dataset (error in ionic balance &lt; 10%).</p>
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<p>Comparative Piper diagrams of the groups formed by k-means and HCA before and after the removal of ionic balance outliers (Note: Group colors in this figure were assigned for clusters visualization only).</p>
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<p>Graphic hydrogeochemical process assessment: (<b>a</b>–<b>d</b>) bivariate plots; (<b>e</b>,<b>f</b>) Na+-normalized plot for HCO<sub>3</sub><sup>−</sup>.</p>
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<p>Chloroalkaline indices of the 40 sampled wells.</p>
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<p>(<b>a</b>) Saturation indices for calcite and fluorite as a function of fluoride concentration; (<b>b</b>) saturation indices for calcite and fluorite classified by cluster.</p>
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<p>Effect of Na<sup>+</sup>/Ca<sup>2+</sup> ratio in fluoride concentration.</p>
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<p>Spatial distributions of arsenic (<b>left</b>) and fluoride (<b>right</b>) in Valle del Guadiana, May–June 2022.</p>
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14 pages, 12256 KiB  
Article
Genesis of Gypsum/Anhydrite in the World-Class Jinding Zn-Pb Deposit, SW China: Constraints from Field Mapping, Petrography, and S-O-Sr Isotope Geochemistry
by Gang Huang, Yu-Cai Song, Liang-Liang Zhuang, Chuan-Dong Xue, Li-Dan Tian and Wei Wu
Minerals 2024, 14(6), 564; https://doi.org/10.3390/min14060564 - 29 May 2024
Viewed by 707
Abstract
The world-class Jinding deposit in SW China has ~15 Mt of Zn and Pb metals combined, in an evaporite dome containing amounts of gypsum/anhydrite. These gypsum and anhydrite are mainly located in limestone breccias (Member I), gypsum-bearing complexes (Member III), and red mélange, [...] Read more.
The world-class Jinding deposit in SW China has ~15 Mt of Zn and Pb metals combined, in an evaporite dome containing amounts of gypsum/anhydrite. These gypsum and anhydrite are mainly located in limestone breccias (Member I), gypsum-bearing complexes (Member III), and red mélange, with some occurring as veins in clast-free sandstone (Member IV) and as fractures/vugs of host rock. The gypsum/anhydrite and dome genesis remain equivocal. The gypsum in limestone breccias and in red mélange with flow texture contains numerous Late Triassic Sanhedong limestone fragments. The δ34S (14.1%–17%), δ18O (9.7%–14.6%), and 87Sr/86Sr ratios (0.706913–0.708711) of these gypsum are close to the S-O-Sr isotopes of the Upper Triassic Sanhedong Formation anhydrite in the Lanping Basin (δ34S = 15.2%–15.9%, δ18O = 10.9%–13.1%, 87Sr/86Sr = 0.707541–0.707967), and are inconsistent with the Paleocene Yunlong Formation gypsum in the Lanping Basin (87Sr/86Sr = 0.709406–0.709845), indicating that these gypsum were derived from the Upper Triassic Sanhedong Formation evaporite but not from the Paleocene Yunlong Formation, and formed as a result of evaporite diapirism. The δ34S (14.3%–14.5%), δ18O (10.1%–10.3%), and 87Sr/86Sr ratios (0.709503–0.709725) of gypsum as gypsum–sand mixtures in gypsum-bearing complexes are similar to the 87Sr/86Sr ratios of gypsum in the Yunlong Formation of the Lanping Basin and Cenozoic basins in the northern part of the Himalayan–Tibetan orogen, suggesting that the material source of this gypsum was derived from the Yunlong Formation, and formed as a result of gypsum–sand diapirism. The gypsum veins in clast-free pillow-shaped mineralized sandstone and the gypsum in host rock fractures and vugs formed after the supergene minerals such as smithsonite. The δ34S (−16.3%~−12.7%) and δ18O (−9.8%~−4.7%) of this gypsum indicate that the gypsum is of supergene origin with sulfate derived from the reoxidation of reduced sulfur. We confirmed that the Jinding dome is genetically related to diapir of the Late-Triassic Sanhedong Formation evaporite. Clast-free sandstone and gypsum-bearing complexes in the dome were produced by diapir of the Paleocene Yunlong Formation unconsolidated gypsum–sand mixtures. Full article
(This article belongs to the Special Issue Ag-Pb-Zn Deposits: Geology and Geochemistry)
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<p>(<b>A</b>) Generalized structural system of the eastern Himalayan–Tibetan orogen. (<b>B</b>) Simplified geologic map showing the distribution of the Zn–Pb deposit and Mesozoic–Cenozoic basins in the Himalayan–Tibetan orogen (modified from [<a href="#B9-minerals-14-00564" class="html-bibr">9</a>,<a href="#B22-minerals-14-00564" class="html-bibr">22</a>]).</p>
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<p>Simplified geological map showing the distribution of Jinding Zn–Pb–Sr deposit and Cu-Zn–Pb–Sr deposits in Lanping Basin (modified after [<a href="#B16-minerals-14-00564" class="html-bibr">16</a>,<a href="#B21-minerals-14-00564" class="html-bibr">21</a>]).</p>
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<p>Geology of the Jinding dome (modified after [<a href="#B9-minerals-14-00564" class="html-bibr">9</a>,<a href="#B23-minerals-14-00564" class="html-bibr">23</a>]). (<b>A</b>) The location of Jinding deposit. (<b>B</b>) Geological map showing the geology and distribution of mineralization in Jinding dome. (<b>C</b>) Cross section (A-A′ section in (<b>A</b>)) of the Jinding open pit and the Paomaping ore block. (<b>D</b>) The geology and mineralization of the Baicaoping ore block and the locations of gypsum/anhydrite samples.</p>
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<p>Geological map showing the distribution of gypsum/anhydrite and mineralized zones in the Jinding open pit with sampling locations. Map location is shown in <a href="#minerals-14-00564-f003" class="html-fig">Figure 3</a>A (modified from [<a href="#B9-minerals-14-00564" class="html-bibr">9</a>,<a href="#B23-minerals-14-00564" class="html-bibr">23</a>]).</p>
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<p>Gypsum and anhydrite in limestone breccia and mélange. (<b>A</b>) Anhydrite with flow texture in Member I limestone breccias in the Middle Unit, the Paomaping underground. (<b>B</b>) Anhydrite in limestone breccias from drill cores, the Baicaoping ore block. (<b>C</b>,<b>D</b>) Gypsum bodies in red mélange from the Jiayashan ore block, containing clasts of the Upper Triassic Sanhedong Formation limestone.</p>
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<p>(<b>A</b>,<b>B</b>) Gypsum mixed with sand in Member III gypsum-bearing complex from the Middle Unit, the Beichang ore block. (<b>C</b>,<b>D</b>) Gypsum in Member IV clast-free pillow-shaped mineralized sandstone from the Middle Unit, the Beichang ore block. (<b>E</b>) Gypsum filling vugs in limestone breccia from the Middle Unit, the Beichang ore block. (<b>F</b>) Gypsum filling vugs in smithsonite, the Beichang ore block.</p>
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<p>The Photomicrograph BSE image of gypsum containing a bit of celestine. (<b>A</b>) The Photomicrograph of gypsum and celestine. (<b>B</b>) The BSE image of gypsum and celestine with the Sr isotpe composition of celestine.</p>
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<p>Strontium isotope compositions of different type gypsum in gypsum in Jinding, Yunlong Formation in Lanping Basin and eastern margin of Himalayan–Tibetan orogen.</p>
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<p>Plot of δ<sup>34</sup>S vs. δ<sup>18</sup>O values of different type gypsum in Jinding.</p>
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<p>Conceptual model of the evolution of evaporite diapirs.</p>
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16 pages, 6404 KiB  
Article
Hydrochemical Characteristics, Mechanisms of Formation, and Sources of Different Water Bodies in the Northwest Coal–Electricity Agglomeration Area
by Xuan Han, Lei Huang, Junli Gan, Mengfan Yang, Guangyan Zhu, Yanna Li and Jiang Xu
Water 2024, 16(11), 1521; https://doi.org/10.3390/w16111521 - 25 May 2024
Viewed by 764
Abstract
Water resources are relatively scarce in Northwest China. Therefore, this study aimed to identify the hydrochemical characteristics and sources of different water bodies in the Northwest Coal–Electricity Agglomeration area, and the utilization of water resources in the region. Hydrochemical diagrams and correlation analysis [...] Read more.
Water resources are relatively scarce in Northwest China. Therefore, this study aimed to identify the hydrochemical characteristics and sources of different water bodies in the Northwest Coal–Electricity Agglomeration area, and the utilization of water resources in the region. Hydrochemical diagrams and correlation analysis were applied to data obtained through the collection of 40, 14, and 42 surface water, shallow groundwater, and deep groundwater samples, respectively. The Positive Definite Matrix Factor Decomposition (PMF) model was used to explore the origins of ions in different water bodies. The results show the following: (1) The rank of anions in surface water, shallow groundwater, and deep groundwater in water bodies of the Bulianta mining area during the wet period according to concentration was as follows: SO42− > Cl > HCO3 > NO3; that of cations was as follows: Na+ > Ca2+ > Mg2+ > K+; (2) The chemical composition of surface water is mainly regulated by the dissolution of evaporites; that of shallow groundwater was regulated by silicates; that of deep groundwater was mainly regulated by the hydrolysis of silicates and the dissolution of evaporites; (3) Four main sources of ions in different water bodies were identified, namely agricultural activities, rock weathering, primary geology, and unknown sources. Two natural factors, namely rock weathering and primary geology, and human activities contributed to 35.2% and 38.8% of ions in shallow groundwater, respectively. Rock weathering and human activities contributed to 20.6% and 63.9% of ions of deeper groundwater, respectively. This study can provide a basis for the conservation and rational planning and utilization of water resources in the Northwest Coal–Electricity Agglomeration area. Full article
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<p>Sampling points and regional geological sketch map of the Patching Tower mining area.</p>
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<p>Physicochemical indices of different water bodies.</p>
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<p>Piper trilinear diagram of different water bodies in the study area.</p>
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<p>The correlation coefficients between ions for surface water (<b>a</b>), shallow groundwater (<b>b</b>), and deep groundwater (<b>c</b>) in the study area.</p>
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<p>Gibbs diagram of surface water, shallow groundwater, and deep groundwater.</p>
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<p>End element diagram of surface water, shallow groundwater, and deep groundwater.</p>
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<p>Ion ratio diagram of surface water, shallow groundwater, and deep groundwater in the study area (<b>a</b>–<b>e</b>) and (<b>f</b>) Chlorine alkali index CAI-I and CAI-II.</p>
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<p>Relationships between the saturation index (SI) and total dissolved solids (TDS) for different minerals and different water body types in the study area.</p>
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<p>Analysis results of surface water sources (<b>a</b>) Positive definite matrix factorization (PMF) model and (<b>b</b>) Scale diagram.</p>
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<p>Analysis results of shallow groundwater sources (<b>a</b>) Positive definite matrix factorization (PMF) model and (<b>b</b>) Scale diagram.</p>
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<p>Analysis results of deep groundwater sources (<b>a</b>) Positive definite matrix factorization (PMF) model and (<b>b</b>) Scale diagram.</p>
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24 pages, 5195 KiB  
Review
A Challenged Evaporite Paradigm?
by Hans Konrad Johnsen, Martin Torvald Hovland and Hakon Rueslatten
Minerals 2024, 14(5), 527; https://doi.org/10.3390/min14050527 - 20 May 2024
Viewed by 871
Abstract
The general subject of this article deals with the term salt. Salt deposits usually contain chlorides, sulphates/gypsum, borates, carbonates, etc., that are seemingly part of the same system. Even though this article mainly presents data and observations on chlorides, which are not easily [...] Read more.
The general subject of this article deals with the term salt. Salt deposits usually contain chlorides, sulphates/gypsum, borates, carbonates, etc., that are seemingly part of the same system. Even though this article mainly presents data and observations on chlorides, which are not easily explained by the present paradigm, it should also prove relevant for the formation of sulphates and other types of salts observed in major salt deposits. The paradigm explaining large salt deposits rests on two pillars governing salt formation and salt deformation. Salt formation is thought to occur vis solar evaporation of seawater in restricted basins. Salt deformation and forming of salt diapirs is thought to occur due to gravity-induced movements. Our review presents peer-reviewed and published data and observations from different authors within different disciplines that challenge the present evaporite paradigm. The current theory/paradigm rests on numerous observations and interpretations in support of it. Adding more observational interpretations in support of the paradigm will not nullify even one observation that contradicts or remains unexplained by the theory. The contradicting evidence must be explained within the present paradigm for it to survive. Significant observations of and within salt deposits are presented, as well as visual and geophysical observations of salinity in crusts and mantles in relevant tectonic settings. In our view, the omnipresent salinity observed in the subsurface needs to be understood and included in the description of a new salt formation mechanism in order to fully explain all features presented herein. Full article
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<p>Seismic section from the Red Sea, west of Brothers Island, showing diapiric structures and bedded salt formed independently of tectonic movements or sediment loading (modified from [<a href="#B25-minerals-14-00527" class="html-bibr">25</a>]. Encircled area shows a diapiric structure below the point of higher sediment loading, inconsistent with the formation of diapiric salt structures due to sediment loading. The interpreted features (faults, etc.) are all by [<a href="#B25-minerals-14-00527" class="html-bibr">25</a>] They might indicate pathways for precipitating brines.</p>
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<p>Conductive zones around the Cascadia subduction zone near Mount Rainier. Warm colours indicate high conductivity. Conductive fluids and melts are seen rising from above the cold, non-conducting, subducting, slab. Observed earthquakes around 20 km depth are indicated by black circles. Modified from [<a href="#B50-minerals-14-00527" class="html-bibr">50</a>].</p>
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<p>Seismic image from the Cascadia region. Horizontal distance from the US east coast is shown. Blue colours represent fast propagating seismic waves and red colours represent slower propagating waves. Earthquake regions are indicated with black circles. A zone of lower seismic velocity is observed above the zone where the earthquakes are located. Mount Rainier volcano is indicated with a red triangle. Modified from [<a href="#B50-minerals-14-00527" class="html-bibr">50</a>].</p>
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<p>Magneto-telluric (MT) networks in the Altiplano, Lake Uyuni area (modified from [<a href="#B53-minerals-14-00527" class="html-bibr">53</a>]). The red rectangle shows the location of <a href="#minerals-14-00527-f005" class="html-fig">Figure 5</a>.</p>
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<p>Measured electric conductivity below the Altiplano along the ANCORP network. Warm colours indicate zones of high electric conductivity (low resistivity). Seismic activity is indicated by black dots (Earthquakes). White, dotted lines indicate the zone of diverging seismic velocity (ALVZ) (modified from [<a href="#B53-minerals-14-00527" class="html-bibr">53</a>]).</p>
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<p>(<b>a</b>) Section of Lake Uyuni, Bolivia, showing numerous pockmark-like, saline springs on the surface. Central pockmark is located at 20°17′40.78″ S, 67°38′09.28″ W. (<b>b</b>) Enlarged view of the central pockmark. Yellow line is 180 metres long. Images obtained from Google Earth 2024.</p>
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<p>Image from the Uturuncu volcano in Bolivia (22°17′24.27″ S, 64°04′39.10″ W) above the APMB (Altiplano-Puna Magma Body). Salt lakes are observed to the east and south of the volcanic centre. Pockmarks, indicating fluid flow from below, are observed in the image (upper right corner). Yellow line represents 10 km (Image obtained from Google Earth 2024).</p>
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<p>Simulation of the saline fluid system around a volcanic magma chamber after 30,000 years. Saline fluids (green arrows) are directed laterally by precipitating solids in the flow path due to mixing with colder meteoric water (blue arrows) from above. Modified from [<a href="#B61-minerals-14-00527" class="html-bibr">61</a>].</p>
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<p>MT image of the fossil subduction zone (Neoproterozoic to the end of the Paleozoic) in western Junggar, China. Numbers indicate distance in km on both axes. Solute-rich fluids are observed at ca. 20 km depth around deep fault zones (red). Regions with confirmed serpentinisation at/near the surface have low conductivity (blue). Modified from [<a href="#B65-minerals-14-00527" class="html-bibr">65</a>] Text in the figure has been enlarged for clarity.</p>
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<p>Conductivity measurements in ca. 2.7 Ga old craton in the Superior Province of Eastern Canada. The depicted section is located in the Malartic region of the Abitibi sub-province. Deep crustal conductivity is observed in zones below 20 km depth. Sub-vertical, conductive zones extend all the way to the surface, along major fault zones. LCC: lower crustal conductive zone. VC4, VC5, and VC6 indicate sub-vertical conductive zones. Modified from [<a href="#B66-minerals-14-00527" class="html-bibr">66</a>]. Ref. [<a href="#B66-minerals-14-00527" class="html-bibr">66</a>] conclude that the preserved high-conductivity anomalies in the mid-lower crust represent an amalgamation of magmatic-hydrothermal and deformational processes that occurred during construction, peak orogenesis, and collapse in the Archean. Conductive zones, extending all the way to the surface, are very likely caused by brines like those sampled by [<a href="#B33-minerals-14-00527" class="html-bibr">33</a>] from the Canadian Shield.</p>
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