horst langer

SUMMARY Infrasound monitoring plays an important role in the framework of the surveillance of Mt. Etna, Europe’s largest active volcano. Compared to seismic monitoring, which is particularly effective for buried sources, infrasound signals mirror the activity of shallow sources like Strombolian explosions or degassing. The interpretation of infrasound signals is difficult to the untrained eye, as we have to account for volcanic and non-volcanic sources. The problem of handling large and complex data sets can be tackled with machine learning, namely pattern recognition techniques. Here, we focus on so-called ‘Unsupervised Learning’, where we identify groups of patterns being similar to each other. The degree of similarity is based on a metric measuring the distance among the features of the patterns. This work aims at the identification of typical regimes of infrasound radiation and their relation to the state of volcanic activity at Mt. Etna. For this goal, we defined features descr...

The European project KnowRISK (Know your city, reduce seISmic risK through non-structural elements) is a European Commission project in the area of Humanitarian Aid and Civil Protection whose primary aim is the reduction of seismic risk by actions on non-structural elements that can be performed by common citizens, thus contributing to promote communities fast recovery and resilience. Even though the main source of victims in earthquakes is the collapse of constructions, damage in nonstructural elements also contributes to increase the death toll. In many situations, especially in developed countries, non-structural damage is the main source of economic losses. And developed societies have become increasingly demanding in what regards the control of economic losses due to seismic events. A key factor for the reduction of economic losses is the protection of non-structural elements, this is, the contents of buildings and non-structural elements of the building, which usually account ...

<p>The dynamics driving an eruption play a crucial role in the impact volcanic activity has on the community at large. The interpretation of geophysical and geochemical changes heralding a volcanic unrest is a fundamental key to forecasting upcoming phenomena. However, the style and intensity of the eruption are difficult to predict, even in open-conduit volcanoes where eruptions can be relatively frequent. This is the case of Etna, in Italy, one of the most active basaltic volcanoes in the world. In 2021, fifty-two lava fountains arose from its Southeast Crater accompanied by lava emissions and ash fallout, which disrupted air and road traffic in numerous Sicilian municipalities. Lava fountains are just one of the typical eruptive styles of Etna. Strombolian activity and lava flows are also relatively frequent here, each with its own characteristics in terms of intensity and social impact.<br>We developed a machine learning (ML) method for the analysis of the seismic data continuously acquired by the local stations of the Etna permanent seismic network, exploiting the spectral characteristics of the signal. Its design started from: i) the need to detect the volcanic hazard, and ii) provide timely and indicative information on possible eruptive scenarios to the Civil Protection and the Authorities. Besides the identification of anomalies in the data, which flag enhanced volcano dynamics in its early stages, we investigate on clues concerning the potential intensity level of eruptive phenomena. The method works in near real time and can effectively contribute to the multidisciplinary analysis of volcanic hazard.</p>

In this paper we report seismological evidence regarding the emplacement of the dike that fed the July 18 - August 9, 2001 lateral eruption at Mt. Etna volcano. The shallow intrusion and the opening of the eruptive fracture system, which mostly occurred during July 12, and July 18, were accompanied by one of the most intense seismic swarms of the last 20 years. A total of 2694 earthquakes (1 £ Md £ 3.9) were recorded from the beginning of the swarm (July 12) to the end of the eruption (August 9). Seismicity shows the upward migration of the dike from the basement to the relatively thin volcanic pile. A clear hypocentral migration was observed, well constraining the upwards propagation of a near-vertical dike, oriented roughly N-S, and located a few kilometers south of the summit region. Earthquake distribution and orientation of the P-axes from focal mechanisms indicate that the swarm was caused by the local stress source related to the dike intrusion.

ABSTRACT Between of November 1999 and January 2000, two microearthquake swarms consist- ing of 16 and 41 events, respectively, occurred close to the site Ramacca, Southeastern Sicily (Italy). The hypocenters are located in the depth range 17-25 km, some kilome- tres northward from the border of the foredeep, below the thrust zone of the Sicily mountain chain (the Appennine - Maghrebide chain). The distribution of the hypocen- ters shows two distinct clusters, and the comparison of the waveforms revealed clearly that the two swarms form two distinct families of multiplet events. The strong simi- larities of the waveforms in the families has led us to carry out a precise relocation relative to the two master events of the families, in order to analyse the geometrical structure of the earthquake clusters and their relation to important tectonic structures of the zone. We applied the cross-correlation method to obtain precise readings of the wave onsets. SH-wave onsets were used instead P-waves, as they showed clear onsets and a good signal-to-noise ratio. The residuals of the relative locations show small val- ues, no more than several meters on average. The extension of the clusters was found to be 400 m and 250 m with respect to focal depth, and 200 m with respect to the hor- izontal extension. In particular the hypocenters of the first cluster delineate clearly a NNE striking plane with almost vertical dip, which matches the focal mechanism ob- tained as a composite solution of all events of the cluster. As the standard location for the Ramacca sequence showed considerable gap angles, indicating an unfavourable station geometry with respect to this events, we examined the stability of our results carrying out a Monte Carlo experiment. Varying the onset times randomly in the range of +/- 5 ms we found variation of the locations in longitude being less than 10 m, and no more than 50 m both in latitude and depth. Nevertheless, the overall geometrical characteristics of the clusters seem not affected seriously by random error. The hori- zontal extension of the first cluster in a NNW can be related major structures in the area such as, among others, the northern continuation of the Scicli line (Azzaro &Bar- bano, 2000). The direction of the P-axes of the focal mechanism of the first cluster is in agreement with compressional directions inferred from bore-hole breakouts found in the Gela Nappe (Ragg et. al, 1999).

Continuous seismic monitoring has achieved a key position in monitoring active volcanoes. However, it comes with the problem of a huge quantity of data difficult to handle. Automatic pattern recognition techniques have proven effective in seismic data processing and, consequently, have been increasingly implemented to solve different tasks. In this paper we investigate the development of the characteristics of the seismic signal on Mt Etna and its relation to regimes of volcanic activity. To this purpose we apply classification methods both with supervisor (Artificial Neural Networks, Support Vector Machine) and without supervisor (cluster analysis). The former "learn" from exemplar patterns, inferring rules to deal with new and/or noisy data to classify, whereas the latter seek for heterogeneities in the data set applying a specific metric. The choice of automatic classification methods is determined by the necessity to solve rather complex discrimination problems using a...

Ground motion scenarios for Mt. Etna are created using synthetic simulations with the program EXSIM. A large data set of weak motion records is exploited to identify important input parameters which govern the modeling of wave propagation effects, such as Q-values, high frequency cut-off and geometrical spreading. These parameters are used in the simulation of ground motion for earthquakes causing severe damage in the area. Two seismotectonic regimes are distinguished. Volcano-tectonic events, though being of limited magnitude (Mmax ca. 5), cause strong ground shaking for their shallow foci. Being rather frequent, these events represent a considerable threat to cities and villages on the flanks of the volcano. A second regime is related to earthquakes with foci in the crust, at depths of 10–30 km, and magnitudes ranging from 6 to 7. In our synthetic scenarios, we chose two examples of volcano-tectonic events, i.e. the October 29, 2002, Bongiardo event (I = VIII) and the May 8, 1914, Linera earthquake (I = IX–X). A further scenario regards the February 20, 1818 event, considered representative for stronger earthquakes with foci in the crust. We were able to reproduce the essential features of the macroseismic field, in particular accounting for the possibility of strong site effects. We learned that stress drop estimated for weak motion events is probably too low to explain the intensity of ground motion during stronger earthquakes. This corresponds to findings reported in the literature claiming an increase of stress drop with earthquake size.

We explore the success rates of detection and classification algorithms as applied to seismic signals from active volcanoes. The subspace detection method has shown some success in identifying repeating (but not identical) signals from seismic swarm sources, as well as pulling out nonvolcanic long period events within subduction zone tremor. We continue the exploration of this technique as applied to both discrete events and variations within volcanic tremor to determine optimal situations for its use. We will demonstrate both three-dimensional and subband applications both on raw waveforms and derived features such as skewness and kurtosis. The application can be used in both a supervised (select templates and compare) as well as unsupervised (cross-compare all samples and apply clustering to the matrix of comparisons). We compare the method to that of the KKAnalysis tool, which uses a self-organizing map approach to unsupervised clustering for feature vectors derived from the seis...

Artificial Intelligence (AI) has found broad applications in volcano observatories worldwide with the aim of reducing volcanic hazard. The need to process larger and larger quantity of data makes indeed AI techniques appealing for monitoring purposes. Tools based on Artificial Neural Networks and Support Vector Machine have proved to be particularly successful in the classification of seismic events and volcanic tremor changes heralding eruptive activity, such as paroxysmal explosions and lava fountaining at Stromboli and Mt Etna, Italy (e.g., Falsaperla et al., 1996; Langer et al., 2009). Moving on from the excellent results obtained from these applications, we present KKAnalysis, a MATLAB based software which combines several unsupervised pattern classification methods, exploiting routines of the SOM Toolbox 2 for MATLAB (http://www.cis.hut.fi/projects/somtoolbox). KKAnalysis is based on Self Organizing Maps (SOM) and clustering methods consisting of K-Means, Fuzzy C-Means, and a ...

A study of volcanic tremor on Stromboli is carried out on the basis of data recorded daily between 1993 and 1995 by a permanent seismic station (STR) located 1.8 km away from the active craters. We also consider the signal of a second station (TF1), which operated for a shorter time span. Changes in the spectral tremor characteristics can be related

Wavelet methods are useful to study the time-varying properties of seismic signals at basaltic volcanoes, where unrest may lack obvious seismic precursors. Here we demonstrate the use of the DWPT, best basis algorithm, and Ljung-Box-Pierce portmanteau test to isolate frequency bands of strong signal in continuous volcanic tremor, denoise tremor data, and determine polarization and centroid locations for volcanic tremor, using methods described in Jones et al.(2005) and Jones et al.(2006). We present results from ...

Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Particularly, volcano-seismic data sets cover decades long local recordings, temporally encompassing different styles of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Intense earthquakes swarms have often heralded effusive activity. However, from the seismic point of view,

Mount Etna, Europe’s largest and most active volcano is situated close to the Metropolitan area of Catania with about 1 Million inhabitants. Continuous monitoring has therefore been carried out for decades. Among the various disciplines infrasound recordings play an important role in this context. Explosive activity near or above ground as well as shallow tremor processes are easier to identify with airborne sound waves than with seismic waves that are significantly scattered and refracted in the volcanic edifice. However, infrasound signals are often affected by noise, especially by wind noise in the summit area.

We present a set of revised ground-motion models (GMMs) for shallow events at Mt. Etna Volcano. The recent occurrence of damaging events, in particular two of the strongest earthquakes ever instrumentally recorded in the area, has required revising previous GMMs, as these failed to match the observations made for events with local magnitude ML>4.3, above all for sites situated close to the epicenter. The dataset now includes 49 seismic events, with a total of 1600 time histories recorded at distances of up to 100 km, and ML ranging from 3.0 to 4.8. The model gives estimates of peak ground acceleration (both horizontal and vertical), peak ground velocity (both horizontal and vertical), and 5% damped horizontal pseudoacceleration response spectral ordinates up to a period of 4 s. GMMs were developed using the functional form proposed by Boore and Atkinson (2008). Furthermore, with a slightly modified approach, we also considered a regression model using a pseudodepth (h) depending ...

The collection of a conspicuous amount of data in volcanic areas is a key for a deeper understanding of the relationships between faulting, diking and superficial volcanic processes. A way to quickly collect huge amounts of data is to analyse photogrammetry-derived models (Digital surface models, orthomosaics and 3D models) using Unmanned Aerial Vehicles (UAVs) to collect all necessary pictures obtaining final models with a texture ground resolution up to 2-3 cm/pix.

In recent years, new approaches for developing earthquake rupture forecasts (ERFs) have been proposed to be used as an input for probabilistic seismic hazard assessment (PSHA). Zone- based approaches with seismicity rates derived from earthquake catalogs are commonly used in many countries as the standard for national seismic hazard models. In Italy, a single zone- based ERF is currently the basis for the official seismic hazard model. In this contribution, we present eleven new ERFs, including five zone-based, two smoothed seismicity-based, two fault- based, and two geodetic-based, used for a new PSH model in Italy. The ERFs were tested against observed seismicity and were subject to an elicitation procedure by a panel of PSHA experts to verify the scientific robustness and consistency of the forecasts with respect to the observations. Tests and elicitation were finalized to weight the ERFs. The results show a good response to the new inputs to observed seismicity in the last few c...

This paper describes the model implementation and presents results of a probabilistic seismic hazard assessment (PSHA) for the Mt Etna volcanic region in Sicily, Italy considering local volcano-tectonic earthquakes. Working in a volcanic region presents new challenges not typically faced in more standard PSHA, which are most broadly due to the nature of the local volcano-tectonic earthquakes, the cone shape of the volcano, and the attenuation properties of seismic waves in the volcanic region. These have been accounted for through the development of a seismic source model that integrates data from different disciplines (historical and instrumental earthquake datasets, tectonic fault data, etc. presented in a companion paper Part I, Azzaro et al., 2017), and by the development and software implementation of original tools for the computation, such as a new ground-motion prediction equation and magnitude-scaling relationship specifically derived for this volcanic area, and the capabil...

<p>Data-driven approaches applied to to large and complex data sets are intriguing, however the results must be revised with a critical attitude. For example, a diagnostic tool may provide hints for a serious disease, or for anomalous conditions potentially indicating an impending natural risk. The demand of a high score of identified anomalies – true positives -  comes together with the request of a low percentage of false positives. Indeed, a high rate of false positives  can ruin the diagnostics. Receiver Operation Curves (ROC) allows us to find a reasonable compromise between the need of accuracy of the diagnostics and robustness with respect to false alerts.</p> <p>In multiclass problems success is commonly measured as the score for which calculated and target classification of patterns matches at best. A high score does not automatically mean that a method is truly effective. Its value becomes questionable, when a random guess leads to a high score as well. The so called “Kappa Statistics” is an elegant way to assess the quality of a classification scheme. We present some case studies demonstrating how such a-posteriori analysis helps corroborate the results.</p> <p>Sometimes  an approach does not lead to the desired success. In thes cases, a sound a-posteriori analysis of the reasons for the failure often provide interesting insights into the problem, Those problems may reside in an inappropriate definition of the targets, inadequate features, etc. Often the problems can be fixed just by adjusting some choices. Finally,  a change of strategy may be necessary in order to achieve a more satisfying result. In the applications presented here, we highlight the pitfalls arising in particular from ill-defined targets and unsuitable feature selections.</p> <p>The validation of unsupervised learning is still a matter of debate. Some formal criteria (e. g. Davies Bouldin Index, Silhouette Index or other) are available for centroid-based clustering where a unique metric valid for all clusters can be defined. Difficulties arise when metrics are defined individually for each single cluster (for instance, Gaussian Model clusters, adaptive criteria) as well as using schemes where centroids are essentially meaningless. This is the case in density based clustering. In all these cases, users are better off when asking themselves whether a clustering is meaningful for the problem in physical terms. In our presentation we discuss the problem of choosing a suitable number of clusters in cases in which formal criteria are not applicable. We demonstrate how the identification of groups of patterns helps the identification of elements which have a clear physical meaning, even when strict rules for assessing the clustering are not available.    </p> <p> </p> <p> </p>

<p>Magma transfer in an open-conduit volcano is a complex process that is still open to debate and not entirely understood. For this reason, a multidisciplinary monitoring of active volcanoes is not only welcome, but also necessary for a correct comprehension of how volcanoes work. Mt. Etna is probably one of the best test sites for doing this, because of the large multidisciplinary monitoring network setup by the Osservatorio Etneo of Istituto Nazionale di Geofisica e Vulcanologia (INGV-OE), the high frequency of eruptions and the relatively easy access to most of its surface.<br />We present new data on integrated monitoring of volcanic tremor, plume sulphur dioxide (SO<sub>2</sub>) flux and soil hydrogen (H<sub>2</sub>) and carbon dioxide (CO<sub>2</sub>) concentration from Mt. Etna. The RMS amplitude of volcanic tremor was measured by seismic stations at various distances from the summit craters, plume SO<sub>2</sub> flux was measured from nine stations around the volcano and soil gases were measured in a station located in a low-temperature (T ∼ 85 °C) fumarole field on the upper north side of the volcano.<br />During our monitoring period, we observed clear and marked anomalous changes in all parameters, with a nice temporal sequence that started with a soil CO<sub>2</sub> and SO<sub>2</sub> flux increase, followed a few days later by a soil H<sub>2</sub> spike-like increase and finally with sharp spike-like increases in RMS amplitude (about 24 h after the onset of the anomaly in H<sub>2</sub>) at all seismic stations.<br />After the initial spikes, all parameters returned more or less slowly to their background levels. Geochemical data, however, showed persistence of slight anomalous degassing for some more weeks, even in the apparent absence of RMS amplitude triggers. This suggests that the conditions of slight instability in the degassing magma column inside the volcano conduits lasted for a long period, probably until return to some sort of balance with the “normal” pressure conditions.<br />The RMS amplitude increase accompanied the onset of strong Strombolian activity at the Northeast Crater, one of the four summit craters of Mt. Etna, which continued during the following period of moderate geochemical anomalies. This suggests a cause-effect relationship between the anomalies observed in all parameters and magma migration inside the central conduits of the volcano. Volcanic tremor is a well-established key parameter in the assessment of the probability of eruptive activity at Etna and it is actually used as a basis for a multistation system for detection of volcanic anomalies that has been developed by INGV-OE at Etna. Adding the information provided by our geochemical parameters gave us more solid support to this system, helping us understand better the mechanisms of magma migration inside of an active, open-conduit basaltic volcano.</p>

We analyze short-to long-term changes (from days to months) in Radon (Rn) activity measured nearby (<2 km) the eruptive fractures that fed a lava effusion at Mt. Etna, Italy, between 13 May 2008 and 6 July 2009. The N120-1408E eruptive fractures opened between 3050 and 2620 m above sea level before a dike-forming intrusion fed the $14 month-long lava emission. Our high-rate data streams include: Rn, ambient parameters (barometric pressure and soil temperature), and seismic data (earthquakes and volcanic tremor) recorded from January 2008 to July 2009. The analysis highlights repeated episodes of rock-fracturing related to seismic swarms, and vigorous gas pulses and peak values in Rn emissions (maximum $4.1310 5 Bq/m 3 on 16 November 2008), which we interpreted in a conceptual model as the response to inputs from the magmatic system during the eruption. This multidisciplinary study: (i) provides evidence of a close relationship between Rn emission at a fumarole near the summit active craters and local earthquakes , and (ii) enables exploring the important role of the volcanic source on the temporal development of the Rn flux, which may account for the much higher ()94 m/d) ascent speed of the Rn carrier (vapor) than diffusion. The close location of Rn probes to the active conduits, along with the application of our multidisciplinary approach, may shed new light on the internal dynamics of other active volcanoes worldwide.

Local site effects, normally ground motion amplification, represent one of the main components when developing ground motion simulations and play an important role in the potential earthquake damage. In the framework of the UPStrat-MAFA project a stochastic finite-fault simulation method was selected for the generation of synthetic ground motion scenarios. This method uses spectral site correction functions to account for site amplification effects. These local effects may undergo significant changes due to the source–receiver configuration (i.e., distance, source depth and ray incidence). This holds in particular for reflection and transmission coefficients which may strongly vary depending on the source–receiver geometry, and may alter the characteristics of the spectral site-correction functions. A strategy is proposed to account for local site effects in the context of the regional geological structure, considering SH-waves propagating in a 1D velocity model. Spectral correction functions are derived by comparing Green's functions obtained for general velocity models and those more detailed at shallow depths. The developed approach is applied in two of the test areas selected in the project, the Mt Etna in Italy and the Vega Baja in SE Spain. The results show the different behaviour in two environments, i.e., volcanic and tectonic, with different seismicity characteristics, and highlight the importance of performing specific site-effect studies in some regions where standard building code soil factors could have some limitations to evaluate the potential for ground motion amplification.

Tremor is commonly addressed to as the RMS-amplitude of the seismic signal measured over a time window within a defined frequency band. On basaltic volcanoes like Mt. Etna, volcanic tremor is recorded as a persistent signal with no clearly identifiable seismic phases. Therefore, the location of its sources with traditional event location methods fails. Following the efforts of the Istituto Nazionale di Geofisica e Vulcanologia (INGV) to increase the number of seismic stations along with data quality and the reliability of data acquisition, we tackle the location problem using the spatial tremor amplitude distribution. Estimates of source location can be obtained using a simplified attenuation law. In this study, we propose a robust method to monitor the migration of tremor sources at Mt. Etna from January 2004 on, including the last eruption, which started on 7 September, 2004 and ended on 8 March, 2005. We estimate the location uncertainty carrying out random experiments with respe...

The seismic network set up in the Iblean Plateau (Southeastern Sicily) in the framework of the POSEIDON project is aimed at the seismic surveillance of the zone, and in particular the identification of faults with enhanced activity. The seismic activity as inferred from the records of local events recorded in the time span September 1999-February 2000 showed an apparent concentration of events in the zone between Augusta and Siracusa. However, the heterogeneity in the distribution of events with daytime suggested that the seismicity maps are severely biased by artificial events, such as quarry explosions. We have distinguished between tectonic earthquakes and quarry blasts by the inspection of waveforms of certain key stations, and by spectral analysis. As a general rule we found that the local tectonic microearthquakes are richer in high frequencies than the quarry blasts. We tested our discrimination based on waveforms and spectra with a data of local events recorded in the time s...