Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia
Volcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particular...
Published in: | Journal of Geophysical Research: Solid Earth |
---|---|
Main Authors: | , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270_1/component/file_5026281/5026270.pdf |
id |
ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5026270 |
---|---|
record_format |
openpolar |
spelling |
ftgfzpotsdam:oai:gfzpublic.gfz-potsdam.de:item_5026270 2024-09-09T19:49:20+00:00 Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia Steinmann, R. Seydoux, L. Journeau, C. Shapiro, N. Campillo, M. 2024 application/pdf https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270_1/component/file_5026281/5026270.pdf eng eng info:eu-repo/semantics/altIdentifier/doi/10.1029/2023JB027167 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270_1/component/file_5026281/5026270.pdf info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ Journal of Geophysical Research: Solid Earth info:eu-repo/semantics/article 2024 ftgfzpotsdam https://doi.org/10.1029/2023JB027167 2024-06-19T01:43:33Z Volcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particularly well for short transients. Applying classification approaches to long‐duration continuous signals containing volcanic tremors, characterized by varying signal characteristics, proves challenging due to the complex nature of these signals. That makes it difficult to attribute them to a single volcanic process and questions the feasibility of classification. In the present study, we consider the whole seismic time series as valuable information about the plumbing system (the combination of plumbing structure and activity distribution). The considered data are year‐long seismograms recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). With a scattering network and a Uniform Manifold Approximation and Projection (UMAP), we transform the continuous data into a two‐dimensional representation (a seismogram atlas), which helps us to identify sudden and continuous changes in the signal properties. We observe an ever‐changing seismic wavefield that we relate to a continuously evolving plumbing system. Through additional data, we can relate signal variations to various state changes of the volcano including transitions from deep to shallow activity, deep reactivation, weak signals during quiet times, and eruptive activity. The atlases serve as a visual tool for analyzing extensive seismic time series, allowing us to associate specific atlas areas, indicative of similar signal characteristics, with distinct volcanic activities and variations in the volcanic plumbing system. Article in Journal/Newspaper Kamchatka GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) Journal of Geophysical Research: Solid Earth 129 3 |
institution |
Open Polar |
collection |
GFZpublic (German Research Centre for Geosciences, Helmholtz-Zentrum Potsdam) |
op_collection_id |
ftgfzpotsdam |
language |
English |
description |
Volcanoes produce a variety of seismic signals and, therefore, continuous seismograms provide crucial information for monitoring the state of a volcano. According to their source mechanism and signal properties, seismo‐volcanic signals can be categorized into distinct classes, which works particularly well for short transients. Applying classification approaches to long‐duration continuous signals containing volcanic tremors, characterized by varying signal characteristics, proves challenging due to the complex nature of these signals. That makes it difficult to attribute them to a single volcanic process and questions the feasibility of classification. In the present study, we consider the whole seismic time series as valuable information about the plumbing system (the combination of plumbing structure and activity distribution). The considered data are year‐long seismograms recorded at individual stations near the Klyuchevskoy Volcanic Group (Kamchatka, Russia). With a scattering network and a Uniform Manifold Approximation and Projection (UMAP), we transform the continuous data into a two‐dimensional representation (a seismogram atlas), which helps us to identify sudden and continuous changes in the signal properties. We observe an ever‐changing seismic wavefield that we relate to a continuously evolving plumbing system. Through additional data, we can relate signal variations to various state changes of the volcano including transitions from deep to shallow activity, deep reactivation, weak signals during quiet times, and eruptive activity. The atlases serve as a visual tool for analyzing extensive seismic time series, allowing us to associate specific atlas areas, indicative of similar signal characteristics, with distinct volcanic activities and variations in the volcanic plumbing system. |
format |
Article in Journal/Newspaper |
author |
Steinmann, R. Seydoux, L. Journeau, C. Shapiro, N. Campillo, M. |
spellingShingle |
Steinmann, R. Seydoux, L. Journeau, C. Shapiro, N. Campillo, M. Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
author_facet |
Steinmann, R. Seydoux, L. Journeau, C. Shapiro, N. Campillo, M. |
author_sort |
Steinmann, R. |
title |
Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
title_short |
Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
title_full |
Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
title_fullStr |
Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
title_full_unstemmed |
Machine Learning Analysis of Seismograms Reveals a Continuous Plumbing System Evolution Beneath the Klyuchevskoy Volcano in Kamchatka, Russia |
title_sort |
machine learning analysis of seismograms reveals a continuous plumbing system evolution beneath the klyuchevskoy volcano in kamchatka, russia |
publishDate |
2024 |
url |
https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270_1/component/file_5026281/5026270.pdf |
genre |
Kamchatka |
genre_facet |
Kamchatka |
op_source |
Journal of Geophysical Research: Solid Earth |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1029/2023JB027167 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270 https://gfzpublic.gfz-potsdam.de/pubman/item/item_5026270_1/component/file_5026281/5026270.pdf |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1029/2023JB027167 |
container_title |
Journal of Geophysical Research: Solid Earth |
container_volume |
129 |
container_issue |
3 |
_version_ |
1809918738739232768 |