Network-based analysis of seismo-volcanic tremors

International audience Volcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as pre...

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Main Authors: Shapiro, Nikolai, Soubestre, Jean, Journeau, Cyril
Other Authors: Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement IRD : UR219-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA)
Format: Book Part
Language:English
Published: HAL CCSD 2023
Subjects:
Online Access:https://insu.hal.science/insu-04167423
https://insu.hal.science/insu-04167423/document
https://insu.hal.science/insu-04167423/file/Volcanic-tremor_Shapiro-Soubestre-Journeau_2023-06-20_Revision-untracked-changes.pdf
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spelling ftccsdartic:oai:HAL:insu-04167423v1 2023-08-20T04:07:40+02:00 Network-based analysis of seismo-volcanic tremors Shapiro, Nikolai, Soubestre, Jean Journeau, Cyril Institut des Sciences de la Terre (ISTerre) Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement IRD : UR219-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA) 2023-06-20 https://insu.hal.science/insu-04167423 https://insu.hal.science/insu-04167423/document https://insu.hal.science/insu-04167423/file/Volcanic-tremor_Shapiro-Soubestre-Journeau_2023-06-20_Revision-untracked-changes.pdf en eng HAL CCSD insu-04167423 https://insu.hal.science/insu-04167423 https://insu.hal.science/insu-04167423/document https://insu.hal.science/insu-04167423/file/Volcanic-tremor_Shapiro-Soubestre-Journeau_2023-06-20_Revision-untracked-changes.pdf info:eu-repo/semantics/OpenAccess IAVCEI Book Modern Volcano Monitoring https://insu.hal.science/insu-04167423 IAVCEI Book Modern Volcano Monitoring, In press [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/bookPart Book sections 2023 ftccsdartic 2023-07-29T22:34:52Z International audience Volcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as precursor signals to eruptions. These signals have been analyzed for several decades with single station approaches, from which empirical inferences can be made regarding their sources, generation mechanism and scaling relations with eruptions parameters. Modernisation and densification of instrumentation networks coupled with sophistication of analysis methods and enhanced computation capacities, allow to switch from single-station to full seismic network based methods. We introduce in this chapter the interstation cross-correlations methods, the estimation of the network covariance matrix and the study of its eigenvalues and eigenvectors. Such advanced methods enable to measure temporal, spatial and spectral tremor properties. They are contained in the CovSeisNet Python package which has been used for characterizing various tremor episodes, including two examples from Kilauea volcano, Hawaii and Klyuchevskoy Volcanic Group, Kamchatka presented in this chapter. These application examples illustrate the complexity of tremors and emphasize the need to continue the development of new algorithms aimed at the exploration of network covariances to better constrain the different tremor generation processes that can be multiple, simultaneous and interacting. In particular, the combination of network-based analysis with polarization and machine learning approaches may represent a new step in our understanding of the underlying phenomena. In turn, this enhanced discernment of the involved processes and the links with the properties of the volcanic system can lead to a more effective monitoring and ultimately a better apprehension of volcanic system destabilizations and anticipation of the associated unrests. Book Part Kamchatka Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe)
institution Open Polar
collection Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe)
op_collection_id ftccsdartic
language English
topic [SDU.STU]Sciences of the Universe [physics]/Earth Sciences
spellingShingle [SDU.STU]Sciences of the Universe [physics]/Earth Sciences
Shapiro, Nikolai,
Soubestre, Jean
Journeau, Cyril
Network-based analysis of seismo-volcanic tremors
topic_facet [SDU.STU]Sciences of the Universe [physics]/Earth Sciences
description International audience Volcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as precursor signals to eruptions. These signals have been analyzed for several decades with single station approaches, from which empirical inferences can be made regarding their sources, generation mechanism and scaling relations with eruptions parameters. Modernisation and densification of instrumentation networks coupled with sophistication of analysis methods and enhanced computation capacities, allow to switch from single-station to full seismic network based methods. We introduce in this chapter the interstation cross-correlations methods, the estimation of the network covariance matrix and the study of its eigenvalues and eigenvectors. Such advanced methods enable to measure temporal, spatial and spectral tremor properties. They are contained in the CovSeisNet Python package which has been used for characterizing various tremor episodes, including two examples from Kilauea volcano, Hawaii and Klyuchevskoy Volcanic Group, Kamchatka presented in this chapter. These application examples illustrate the complexity of tremors and emphasize the need to continue the development of new algorithms aimed at the exploration of network covariances to better constrain the different tremor generation processes that can be multiple, simultaneous and interacting. In particular, the combination of network-based analysis with polarization and machine learning approaches may represent a new step in our understanding of the underlying phenomena. In turn, this enhanced discernment of the involved processes and the links with the properties of the volcanic system can lead to a more effective monitoring and ultimately a better apprehension of volcanic system destabilizations and anticipation of the associated unrests.
author2 Institut des Sciences de la Terre (ISTerre)
Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement IRD : UR219-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA)
format Book Part
author Shapiro, Nikolai,
Soubestre, Jean
Journeau, Cyril
author_facet Shapiro, Nikolai,
Soubestre, Jean
Journeau, Cyril
author_sort Shapiro, Nikolai,
title Network-based analysis of seismo-volcanic tremors
title_short Network-based analysis of seismo-volcanic tremors
title_full Network-based analysis of seismo-volcanic tremors
title_fullStr Network-based analysis of seismo-volcanic tremors
title_full_unstemmed Network-based analysis of seismo-volcanic tremors
title_sort network-based analysis of seismo-volcanic tremors
publisher HAL CCSD
publishDate 2023
url https://insu.hal.science/insu-04167423
https://insu.hal.science/insu-04167423/document
https://insu.hal.science/insu-04167423/file/Volcanic-tremor_Shapiro-Soubestre-Journeau_2023-06-20_Revision-untracked-changes.pdf
genre Kamchatka
genre_facet Kamchatka
op_source IAVCEI Book Modern Volcano Monitoring
https://insu.hal.science/insu-04167423
IAVCEI Book Modern Volcano Monitoring, In press
op_relation insu-04167423
https://insu.hal.science/insu-04167423
https://insu.hal.science/insu-04167423/document
https://insu.hal.science/insu-04167423/file/Volcanic-tremor_Shapiro-Soubestre-Journeau_2023-06-20_Revision-untracked-changes.pdf
op_rights info:eu-repo/semantics/OpenAccess
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