Readings from a sensor for the radioactive gas near summit craters of the Italian volcano reveal ... more Readings from a sensor for the radioactive gas near summit craters of the Italian volcano reveal signatures of such processes as seismic rock fracturing and sloshing of groundwater and other fluids.
SUMMARY Infrasound monitoring plays an important role in the framework of the surveillance of Mt.... more 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 element... more 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 volca... more <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... more 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... more 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).
Journal of Volcanology and Geothermal Research, 2006
Seismic energy radiation at Soufrière Hills volcano, Montserrat, is made up by various types of t... more Seismic energy radiation at Soufrière Hills volcano, Montserrat, is made up by various types of transient signals, which are distinguished by the Montserrat Volcano Observatory (MVO) in different classes with respect to their characteristics and/or origin. There are five fundamental classes, i.e., Volcano-Tectonic Events, Regional Events, Long-Period Events, Hybrid Events, and Rockfalls. Identification and classification of these transients, which have been hitherto carried out manually by various staff members, yield important information for the assessment of the state of the volcano system. In the frame of the MULTIMO project, we proposed the application of Artificial Neural Networks (ANN) for the classification of these kind of data in order to handle large data sets, and to achieve reproducible results, emulating the expert's analysis. Using the manual routine classification as a-priori information, we obtained a fair performance of such an automatic processing, with 70% of the automatic classifications being consistent with the original ones. From an analysis of the misclassified events, however, we found that for most of them the original a-priori classification was incorrect. In this study, we first revised manually the original a-priori classification. Based on a data set of 6000 events, we carried out a reanalysis of the seismic traces recorded at different seismic stations. Then, using this new information, we trained and tested the ANN, obtaining a successful classification in ca. 80% of records. Particularly, the automatic classification was excellent in the identification of Rockfalls and Volcano-Tectonic Events. Among the misfits, we observe the erroneous attribution of Long-Period and Hybrid Events to Rockfalls. This may be partly explained by the fact that signals addressed to as Rockfalls contain frequently contributions of various sources. Overall, the failure in the classification between some types of transients highlights the problem of the concurrent activation and/or unclear separation of distinctive sources from which the signals stem. We conclude that the automatic classification with ANN is a powerful tool for handling large data masses as well as for the a-posterior analysis of the consistency of the classification problem.
The calibration of input parameters is an important task for stochastic finite-fault simulation i... more The calibration of input parameters is an important task for stochastic finite-fault simulation in volcanic areas, and we manage this in the framework of the European project UPStrat-MaFa. The stochastic simulation method requires the knowledge of fault geometry, source, crust properties of the region, and local site effects. At first, we focused the present study in the pilot test areas: Mt Vesuvius, Campi Flegrei and Mt Etna. Later, we performed two applications for a large magnitude event in the Azores Islands and the South Iceland regions. A general preliminary database of ground-motion records was collected in the test areas, to set up the empirical laws of the ground-motion parameters. The results of the simulations have been compared with observed waveforms and response spectra, to determine the suitability of the parameters used. The results show good agreement between the observed and simulated time histories and response spectra, thus encouraging further efforts towards quantitative high resolution studies on input parameters.
Continuous seismic monitoring has achieved a key position in monitoring active volcanoes. However... more 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...
The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visit... more The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visiting the summit craters as well as to the nearby population and thus requires constant monitoring. In...
We collected drone data to quantify the kinematics at extensional fractures and normal faults, in... more We collected drone data to quantify the kinematics at extensional fractures and normal faults, integrated this information with seismological data to reconstruct the stress field, and critically compared the results with previous fieldwork to assess the best practice. As a key site, we analyzed a sector of the northeast rift of Mt Etna, an area affected by continuous ground deformation linked to gravity sliding of the volcano's eastern flank and dike injections. The studied sector is characterized also by the existence of eruptive craters and fissures and lava flows. This work shows that this rift segment is affected by a series of NNE-to NE-striking, parallel extensional fractures characterized by an opening mode along an average N105.7 • vector. The stress field is characterized by a σ Hmin trending northwest-southeast. Normal faults strike parallel to the extensional fractures. The extensional strain obtained by cumulating the net offset at extensional fractures with the fault heave gives a stretching ratio of 1.003 in the northeastern part of the study area and 1.005 in the southwestern part. Given a maximum age of 1614 CE for the offset lavas, we obtained an extension rate of 1.9 cm yr −1 for the last 406 years. This value is consistent with the slip along the Pernicana Fault system, confirming that the NE rift structures accommodate the sliding of the eastern flank of the volcano.
early-warning assessment of a volcanic unrest requires that accurate information from monitoring ... more early-warning assessment of a volcanic unrest requires that accurate information from monitoring is continuously gathered before volcanic activity starts. seismic data are an optimal source of such information, overcoming safety problems due to dangerous conditions for field surveys or cloud cover that may hinder visibility. We designed a multi-station warning system based on the classification of patterns of the background seismic radiation, so-called volcanic tremor, by using self-organizing Maps (SOM) and fuzzy clustering. The classifier automatically detects patterns that are typical footprints of volcanic unrest. the issuance of the soM colors on DeM allows their geographical visualization according to the stations of detection; this spatial location makes it possible to infer areas potentially impacted by eruptive phenomena. Tested at Mt. Etna (Italy), the classifier forecasted in hindsight patterns associated with fast-rising magma (typical of lava fountains) as well as a relatively long lead time of the outburst (lava flows from eruptive fractures). Receiver Operating Characteristics (ROC) curves gave an Area Under the Curve (AUC) ∼0.8 indicative of a good detection accuracy that cannot be achieved from a mere random choice.
Ground motion scenarios for Mt. Etna are created using synthetic simulations with the program EXS... more 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 sig... more 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...
Readings from a sensor for the radioactive gas near summit craters of the Italian volcano reveal ... more Readings from a sensor for the radioactive gas near summit craters of the Italian volcano reveal signatures of such processes as seismic rock fracturing and sloshing of groundwater and other fluids.
SUMMARY Infrasound monitoring plays an important role in the framework of the surveillance of Mt.... more 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 element... more 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 volca... more <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... more 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... more 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).
Journal of Volcanology and Geothermal Research, 2006
Seismic energy radiation at Soufrière Hills volcano, Montserrat, is made up by various types of t... more Seismic energy radiation at Soufrière Hills volcano, Montserrat, is made up by various types of transient signals, which are distinguished by the Montserrat Volcano Observatory (MVO) in different classes with respect to their characteristics and/or origin. There are five fundamental classes, i.e., Volcano-Tectonic Events, Regional Events, Long-Period Events, Hybrid Events, and Rockfalls. Identification and classification of these transients, which have been hitherto carried out manually by various staff members, yield important information for the assessment of the state of the volcano system. In the frame of the MULTIMO project, we proposed the application of Artificial Neural Networks (ANN) for the classification of these kind of data in order to handle large data sets, and to achieve reproducible results, emulating the expert's analysis. Using the manual routine classification as a-priori information, we obtained a fair performance of such an automatic processing, with 70% of the automatic classifications being consistent with the original ones. From an analysis of the misclassified events, however, we found that for most of them the original a-priori classification was incorrect. In this study, we first revised manually the original a-priori classification. Based on a data set of 6000 events, we carried out a reanalysis of the seismic traces recorded at different seismic stations. Then, using this new information, we trained and tested the ANN, obtaining a successful classification in ca. 80% of records. Particularly, the automatic classification was excellent in the identification of Rockfalls and Volcano-Tectonic Events. Among the misfits, we observe the erroneous attribution of Long-Period and Hybrid Events to Rockfalls. This may be partly explained by the fact that signals addressed to as Rockfalls contain frequently contributions of various sources. Overall, the failure in the classification between some types of transients highlights the problem of the concurrent activation and/or unclear separation of distinctive sources from which the signals stem. We conclude that the automatic classification with ANN is a powerful tool for handling large data masses as well as for the a-posterior analysis of the consistency of the classification problem.
The calibration of input parameters is an important task for stochastic finite-fault simulation i... more The calibration of input parameters is an important task for stochastic finite-fault simulation in volcanic areas, and we manage this in the framework of the European project UPStrat-MaFa. The stochastic simulation method requires the knowledge of fault geometry, source, crust properties of the region, and local site effects. At first, we focused the present study in the pilot test areas: Mt Vesuvius, Campi Flegrei and Mt Etna. Later, we performed two applications for a large magnitude event in the Azores Islands and the South Iceland regions. A general preliminary database of ground-motion records was collected in the test areas, to set up the empirical laws of the ground-motion parameters. The results of the simulations have been compared with observed waveforms and response spectra, to determine the suitability of the parameters used. The results show good agreement between the observed and simulated time histories and response spectra, thus encouraging further efforts towards quantitative high resolution studies on input parameters.
Continuous seismic monitoring has achieved a key position in monitoring active volcanoes. However... more 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...
The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visit... more The frequent activity taking place at Mount Etna may pose risks to tourists and specialists visiting the summit craters as well as to the nearby population and thus requires constant monitoring. In...
We collected drone data to quantify the kinematics at extensional fractures and normal faults, in... more We collected drone data to quantify the kinematics at extensional fractures and normal faults, integrated this information with seismological data to reconstruct the stress field, and critically compared the results with previous fieldwork to assess the best practice. As a key site, we analyzed a sector of the northeast rift of Mt Etna, an area affected by continuous ground deformation linked to gravity sliding of the volcano's eastern flank and dike injections. The studied sector is characterized also by the existence of eruptive craters and fissures and lava flows. This work shows that this rift segment is affected by a series of NNE-to NE-striking, parallel extensional fractures characterized by an opening mode along an average N105.7 • vector. The stress field is characterized by a σ Hmin trending northwest-southeast. Normal faults strike parallel to the extensional fractures. The extensional strain obtained by cumulating the net offset at extensional fractures with the fault heave gives a stretching ratio of 1.003 in the northeastern part of the study area and 1.005 in the southwestern part. Given a maximum age of 1614 CE for the offset lavas, we obtained an extension rate of 1.9 cm yr −1 for the last 406 years. This value is consistent with the slip along the Pernicana Fault system, confirming that the NE rift structures accommodate the sliding of the eastern flank of the volcano.
early-warning assessment of a volcanic unrest requires that accurate information from monitoring ... more early-warning assessment of a volcanic unrest requires that accurate information from monitoring is continuously gathered before volcanic activity starts. seismic data are an optimal source of such information, overcoming safety problems due to dangerous conditions for field surveys or cloud cover that may hinder visibility. We designed a multi-station warning system based on the classification of patterns of the background seismic radiation, so-called volcanic tremor, by using self-organizing Maps (SOM) and fuzzy clustering. The classifier automatically detects patterns that are typical footprints of volcanic unrest. the issuance of the soM colors on DeM allows their geographical visualization according to the stations of detection; this spatial location makes it possible to infer areas potentially impacted by eruptive phenomena. Tested at Mt. Etna (Italy), the classifier forecasted in hindsight patterns associated with fast-rising magma (typical of lava fountains) as well as a relatively long lead time of the outburst (lava flows from eruptive fractures). Receiver Operating Characteristics (ROC) curves gave an Area Under the Curve (AUC) ∼0.8 indicative of a good detection accuracy that cannot be achieved from a mere random choice.
Ground motion scenarios for Mt. Etna are created using synthetic simulations with the program EXS... more 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 sig... more 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...
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