Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields
Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded encouraging results in detecting these changes at active volcanoes. Here, we an...
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ftsubggeo:oai:e-docs.geo-leo.de:11858/10774 2023-07-16T03:59:19+02:00 Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields Makus, Peter Sens‐Schönfelder, Christoph Illien, Luc Walter, Thomas R. Yates, Alexander Tilmann, Frederik 1 Helmholtz Center German Research Center for Geosciences GFZ Potsdam Germany 4 University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France 2023-03-30 https://doi.org/10.1029/2022JB025738 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10774 eng eng doi:10.1029/2022JB025738 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10774 This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. ddc:551 seismology volcano monitoring machine learning ambient noise seismic velocity change time varying earth structure doc-type:article 2023 ftsubggeo https://doi.org/10.1029/2022JB025738 2023-06-25T22:12:18Z Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded encouraging results in detecting these changes at active volcanoes. Here, we analyze seismic data recorded at the Klyuchevskoy Volcanic Group in Kamchatka, Russia, between summer of 2015 and summer of 2016 to study signals related to volcanic activity. However, ubiquitous volcanic tremors introduce distortions in the noise wavefield that cause artifacts in the dv/v $dv/v$ estimates masking the impact of physical mechanisms. To avoid such instabilities, we propose a new technique called time‐segmented passive image interferometry. In this technique, we employ a hierarchical clustering algorithm to find periods in which the wavefield can be considered stationary. For these periods, we perform separate noise interferometry studies. To further increase the temporal resolution of our results, we use an AI‐driven approach to find stations with similar dv/v $dv/v$ responses and apply a spatial stack. The impacts of snow load and precipitation dominate the resulting dv/v $dv/v$ time series, as we demonstrate with the help of a simple model. In February 2016, we observe an abrupt velocity drop due to the M7.2 Zhupanov earthquake. Shortly after, we register a gradual velocity increase of about 0.3% at Bezymianny Volcano coinciding with surface deformation observed using remote sensing techniques. We suggest that the inflation of a shallow reservoir related to the beginning of Bezymianny's 2016/2017 eruptive cycle could have caused this local velocity increase and a decorrelation of the correlation function coda. Plain Language Summary: Before eruptions, volcanoes inflate due to the rising magma from below. Previous studies have found that these deformations can lead to small changes in the properties of the surrounding rock. We use passive image interferometry, a method that relies on the omnipresent ... Article in Journal/Newspaper Kamchatka GEO-LEOe-docs (FID GEO) Journal of Geophysical Research: Solid Earth 128 4 |
institution |
Open Polar |
collection |
GEO-LEOe-docs (FID GEO) |
op_collection_id |
ftsubggeo |
language |
English |
topic |
ddc:551 seismology volcano monitoring machine learning ambient noise seismic velocity change time varying earth structure |
spellingShingle |
ddc:551 seismology volcano monitoring machine learning ambient noise seismic velocity change time varying earth structure Makus, Peter Sens‐Schönfelder, Christoph Illien, Luc Walter, Thomas R. Yates, Alexander Tilmann, Frederik 1 Helmholtz Center German Research Center for Geosciences GFZ Potsdam Germany 4 University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
topic_facet |
ddc:551 seismology volcano monitoring machine learning ambient noise seismic velocity change time varying earth structure |
description |
Volcanic inflation and deflation often precede eruptions and can lead to seismic velocity changes (dv/v $dv/v$) in the subsurface. Recently, interferometry on the coda of ambient noise‐cross‐correlation functions yielded encouraging results in detecting these changes at active volcanoes. Here, we analyze seismic data recorded at the Klyuchevskoy Volcanic Group in Kamchatka, Russia, between summer of 2015 and summer of 2016 to study signals related to volcanic activity. However, ubiquitous volcanic tremors introduce distortions in the noise wavefield that cause artifacts in the dv/v $dv/v$ estimates masking the impact of physical mechanisms. To avoid such instabilities, we propose a new technique called time‐segmented passive image interferometry. In this technique, we employ a hierarchical clustering algorithm to find periods in which the wavefield can be considered stationary. For these periods, we perform separate noise interferometry studies. To further increase the temporal resolution of our results, we use an AI‐driven approach to find stations with similar dv/v $dv/v$ responses and apply a spatial stack. The impacts of snow load and precipitation dominate the resulting dv/v $dv/v$ time series, as we demonstrate with the help of a simple model. In February 2016, we observe an abrupt velocity drop due to the M7.2 Zhupanov earthquake. Shortly after, we register a gradual velocity increase of about 0.3% at Bezymianny Volcano coinciding with surface deformation observed using remote sensing techniques. We suggest that the inflation of a shallow reservoir related to the beginning of Bezymianny's 2016/2017 eruptive cycle could have caused this local velocity increase and a decorrelation of the correlation function coda. Plain Language Summary: Before eruptions, volcanoes inflate due to the rising magma from below. Previous studies have found that these deformations can lead to small changes in the properties of the surrounding rock. We use passive image interferometry, a method that relies on the omnipresent ... |
format |
Article in Journal/Newspaper |
author |
Makus, Peter Sens‐Schönfelder, Christoph Illien, Luc Walter, Thomas R. Yates, Alexander Tilmann, Frederik 1 Helmholtz Center German Research Center for Geosciences GFZ Potsdam Germany 4 University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France |
author_facet |
Makus, Peter Sens‐Schönfelder, Christoph Illien, Luc Walter, Thomas R. Yates, Alexander Tilmann, Frederik 1 Helmholtz Center German Research Center for Geosciences GFZ Potsdam Germany 4 University Grenoble Alpes University Savoie Mont Blanc CNRS IRD University Gustave Eiffel Grenoble France |
author_sort |
Makus, Peter |
title |
Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
title_short |
Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
title_full |
Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
title_fullStr |
Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
title_full_unstemmed |
Deciphering the Whisper of Volcanoes: Monitoring Velocity Changes at Kamchatka's Klyuchevskoy Group With Fluctuating Noise Fields |
title_sort |
deciphering the whisper of volcanoes: monitoring velocity changes at kamchatka's klyuchevskoy group with fluctuating noise fields |
publishDate |
2023 |
url |
https://doi.org/10.1029/2022JB025738 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10774 |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_relation |
doi:10.1029/2022JB025738 http://resolver.sub.uni-goettingen.de/purl?gldocs-11858/10774 |
op_rights |
This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
op_doi |
https://doi.org/10.1029/2022JB025738 |
container_title |
Journal of Geophysical Research: Solid Earth |
container_volume |
128 |
container_issue |
4 |
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1771546953108160512 |