A volcano is a complex system, and the characterization of its state at any given time is not an ... more A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application ...
Volcano-seismic recognition - <strong>VSR</strong> software supporting the <em>... more Volcano-seismic recognition - <strong>VSR</strong> software supporting the <em>VULCAN.ears</em> EU-funded project via H2020-MSCA-IF-2016 Grant This framework has been developed using Python.3 scientific libraries and wxPython.4 graphical widgets, being composed of: <strong><em>pyVERSO</em></strong> - Command Line Interface (CLI) to create and evaluate VSR models given a labelled DB <em>geoStudio</em> - Graphical User Interface (GUI) acting as a frontend to perform Volcano-Independent VSR (VI.VSR) and volcano-seismic data analysis and visualization <em>liveVSR</em> - tool to remotely perform a VSR monitoring in real-time of any volcano accessible via FDSN servers A working snapshot of <em><strong>pyVERSO</strong></em> scripts is freely distributed in this package, in order to build own VSR-models of volcano-seismic classes given a labelled database of a given volcano. Once they're buil...
Preprint of the article: <strong><em>"Standardization of noisy volcano-seismic w... more Preprint of the article: <strong><em>"Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition" </em></strong> publshed in Seismological Research Letters-2019 (https://doi.org/10.1785/0220180334). This work is a 'deliverable' of the <em>VULCAN.ears</em> EC-funded project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).
This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and... more This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article: "<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>", published in the Seismological Research Letters (https://doi.org/10.1785/0220180334). The authors want to thank everyone at the Instituto Andaluz of Geofísica (http://iagpds.ugr.es), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which have made possible the monitoring of Deception Island since early 1990s. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).
Seismic activity in volcanic settings could be the signature of processes that include magma dyna... more Seismic activity in volcanic settings could be the signature of processes that include magma dynamics, hydrothermal activity and geodynamics. The main goal of this study is to analyze the seismicity of Lipari Island (Southern Tyrrhenian Sea) to characterize the dynamic processes such as the interaction between pre-existing structures and hydrothermal processes affecting the Aeolian Islands. We deployed a dense seismic array of 48 autonomous 3-component nodes. For the first time, Lipari and its hydrothermal field are investigated by a seismic array recording continuously for about a month in late 2018 with a 0.1–1.5 km station spacing. We investigate the distribution and evolution of the seismicity over the full time of the experiment using self-organized maps and automatic algorithms. We show that the sea wave motion strongly influences the background seismic noise. Using an automatic template matching approach, we detect and locate a seismic swarm offshore the western coast of Lipa...
Improving the ability to detect and characterize long-duration volcanic tremor is crucial to unde... more Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the long-term dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the...
Updates in Volcanology - Transdisciplinary Nature of Volcano Science, 2021
A volcano is a complex system, and the characterization of its state at any given time is not an ... more A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application ...
The 2012–2016 White Island (Whakaari) eruption sequence encompassed six small explosive events th... more The 2012–2016 White Island (Whakaari) eruption sequence encompassed six small explosive events that included one steam driven and five explosive phreato-magmatic eruptions. More enigmatic, a dome was observed at the back of the vent and crater lake in November 2012. Its emplacement date could not be easily determined due to persistent steam from the evaporating crater lake and because of the very low levels of discrete volcanic earthquakes associated with its growth. During this period, seismicity also included persistent tremor with dominant frequencies in the 2–5 Hz range. Detailed assessment of the tremor reveals a very slow evolution of the spectral peaks from low to higher frequencies. These gliding spectral lines evolved over a three-month time period beginning in late September 2012 and persisting until early January 2013, when the tremor stabilised. As part of the dome emplacement episode, the crater lake progressively dried, leaving isolated pools which then promoted persis...
A volcano is a complex system, and the characterization of its state at any given time is not an ... more A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application ...
Volcano-seismic recognition - <strong>VSR</strong> software supporting the <em>... more Volcano-seismic recognition - <strong>VSR</strong> software supporting the <em>VULCAN.ears</em> EU-funded project via H2020-MSCA-IF-2016 Grant This framework has been developed using Python.3 scientific libraries and wxPython.4 graphical widgets, being composed of: <strong><em>pyVERSO</em></strong> - Command Line Interface (CLI) to create and evaluate VSR models given a labelled DB <em>geoStudio</em> - Graphical User Interface (GUI) acting as a frontend to perform Volcano-Independent VSR (VI.VSR) and volcano-seismic data analysis and visualization <em>liveVSR</em> - tool to remotely perform a VSR monitoring in real-time of any volcano accessible via FDSN servers A working snapshot of <em><strong>pyVERSO</strong></em> scripts is freely distributed in this package, in order to build own VSR-models of volcano-seismic classes given a labelled database of a given volcano. Once they're buil...
Preprint of the article: <strong><em>"Standardization of noisy volcano-seismic w... more Preprint of the article: <strong><em>"Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition" </em></strong> publshed in Seismological Research Letters-2019 (https://doi.org/10.1785/0220180334). This work is a 'deliverable' of the <em>VULCAN.ears</em> EC-funded project. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).
This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and... more This dataset contains required volcano-seismic waveform DBs (<em>dec.95M.16c</em> and <em>dec.09U.4c</em>) used in the article: "<em>Standardization of noisy volcano-seismic waveforms as a key step towards station-independent, robust automatic recognition</em>", published in the Seismological Research Letters (https://doi.org/10.1785/0220180334). The authors want to thank everyone at the Instituto Andaluz of Geofísica (http://iagpds.ugr.es), precisely to Prof. Jesús Ibáñez and Dr. Javier Almendros, IPs of several research projects which have made possible the monitoring of Deception Island since early 1990s. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No.[749249] (VULCAN.ears).
Seismic activity in volcanic settings could be the signature of processes that include magma dyna... more Seismic activity in volcanic settings could be the signature of processes that include magma dynamics, hydrothermal activity and geodynamics. The main goal of this study is to analyze the seismicity of Lipari Island (Southern Tyrrhenian Sea) to characterize the dynamic processes such as the interaction between pre-existing structures and hydrothermal processes affecting the Aeolian Islands. We deployed a dense seismic array of 48 autonomous 3-component nodes. For the first time, Lipari and its hydrothermal field are investigated by a seismic array recording continuously for about a month in late 2018 with a 0.1–1.5 km station spacing. We investigate the distribution and evolution of the seismicity over the full time of the experiment using self-organized maps and automatic algorithms. We show that the sea wave motion strongly influences the background seismic noise. Using an automatic template matching approach, we detect and locate a seismic swarm offshore the western coast of Lipa...
Improving the ability to detect and characterize long-duration volcanic tremor is crucial to unde... more Improving the ability to detect and characterize long-duration volcanic tremor is crucial to understand the long-term dynamics and unrest of volcanic systems. We have applied data reduction methods (permutation entropy and polarization degree, among others) to characterize the seismic wave field near Copahue volcano (Southern Andes) between June 2012 and January 2013, when phreatomagmatic episodes occurred. During the selected period, a total of 52 long-duration events with energy above the background occurred. Among them, 32 were classified as volcanic tremors and the remaining as noise bursts. Characterizing each event by averaging its reduced parameters, allowed us to study the range of variability of the different events types. We found that, compared to noise burst, tremors have lower permutation entropies and higher dominant polarization degrees. This characterization is a suitable tool for detecting long-duration volcanic tremors in the ambient seismic wave field, even if the...
Updates in Volcanology - Transdisciplinary Nature of Volcano Science, 2021
A volcano is a complex system, and the characterization of its state at any given time is not an ... more A volcano is a complex system, and the characterization of its state at any given time is not an easy task. Monitoring data can be used to estimate the probability of an unrest and/or an eruption episode. These can include seismic, magnetic, electromagnetic, deformation, infrasonic, thermal, geochemical data or, in an ideal situation, a combination of them. Merging data of different origins is a non-trivial task, and often even extracting few relevant and information-rich parameters from a homogeneous time series is already challenging. The key to the characterization of volcanic regimes is in fact a process of data reduction that should produce a relatively small vector of features. The next step is the interpretation of the resulting features, through the recognition of similar vectors and for example, their association to a given state of the volcano. This can lead in turn to highlight possible precursors of unrests and eruptions. This final step can benefit from the application ...
The 2012–2016 White Island (Whakaari) eruption sequence encompassed six small explosive events th... more The 2012–2016 White Island (Whakaari) eruption sequence encompassed six small explosive events that included one steam driven and five explosive phreato-magmatic eruptions. More enigmatic, a dome was observed at the back of the vent and crater lake in November 2012. Its emplacement date could not be easily determined due to persistent steam from the evaporating crater lake and because of the very low levels of discrete volcanic earthquakes associated with its growth. During this period, seismicity also included persistent tremor with dominant frequencies in the 2–5 Hz range. Detailed assessment of the tremor reveals a very slow evolution of the spectral peaks from low to higher frequencies. These gliding spectral lines evolved over a three-month time period beginning in late September 2012 and persisting until early January 2013, when the tremor stabilised. As part of the dome emplacement episode, the crater lake progressively dried, leaving isolated pools which then promoted persis...
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Papers by Roberto Carniel