Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

International audience In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice t...

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Published in:The Cryosphere
Main Authors: Moreau, Ludovic, Seydoux, Léonard, Weiss, Jérôme, Campillo, Michel
Other Authors: Institut des Sciences de la Terre (ISTerre), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-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), Institut de Physique du Globe de Paris (IPGP (UMR_7154)), Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), European Project: No. 742335,ERC F-IMAGE
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2023
Subjects:
Online Access:https://hal.science/hal-04041306
https://hal.science/hal-04041306/document
https://hal.science/hal-04041306/file/Moreau_etal_Cryo-2023.pdf
https://doi.org/10.5194/tc-17-1327-2023
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spelling ftunivparis:oai:HAL:hal-04041306v1 2024-05-19T07:36:41+00:00 Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring Moreau, Ludovic Seydoux, Léonard Weiss, Jérôme Campillo, Michel Institut des Sciences de la Terre (ISTerre) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-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) Institut de Physique du Globe de Paris (IPGP (UMR_7154)) Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité) ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019) European Project: No. 742335,ERC F-IMAGE 2023 https://hal.science/hal-04041306 https://hal.science/hal-04041306/document https://hal.science/hal-04041306/file/Moreau_etal_Cryo-2023.pdf https://doi.org/10.5194/tc-17-1327-2023 en eng HAL CCSD Copernicus info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-1327-2023 info:eu-repo/grantAgreement//No. 742335/EU/Seismic Functional Imaging of the Brittle Crust/ERC F-IMAGE hal-04041306 https://hal.science/hal-04041306 https://hal.science/hal-04041306/document https://hal.science/hal-04041306/file/Moreau_etal_Cryo-2023.pdf doi:10.5194/tc-17-1327-2023 info:eu-repo/semantics/OpenAccess ISSN: 1994-0424 EISSN: 1994-0416 The Cryosphere https://hal.science/hal-04041306 The Cryosphere, 2023, 17 (3), pp.1327-1341. ⟨10.5194/tc-17-1327-2023⟩ [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology info:eu-repo/semantics/article Journal articles 2023 ftunivparis https://doi.org/10.5194/tc-17-1327-2023 2024-04-23T03:32:47Z International audience In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness, will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen Fjord (Svalbard), we observe the spatial clustering of icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial seismic sources, we were able to derive a power law of icequake energy and to relate this energy to the size of the cracks that generate the icequakes. Article in Journal/Newspaper Arctic Sea ice Svalbard The Cryosphere Université de Paris: Portail HAL The Cryosphere 17 3 1327 1341
institution Open Polar
collection Université de Paris: Portail HAL
op_collection_id ftunivparis
language English
topic [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology
spellingShingle [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology
Moreau, Ludovic
Seydoux, Léonard
Weiss, Jérôme
Campillo, Michel
Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
topic_facet [SDU.STU.GL]Sciences of the Universe [physics]/Earth Sciences/Glaciology
description International audience In the perspective of an upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness, will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequake waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen Fjord (Svalbard), we observe the spatial clustering of icequake sources along the shoreline of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the 4 weeks of data recording. Comparing the energy of the icequakes with that of artificial seismic sources, we were able to derive a power law of icequake energy and to relate this energy to the size of the cracks that generate the icequakes.
author2 Institut des Sciences de la Terre (ISTerre)
Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-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)
Institut de Physique du Globe de Paris (IPGP (UMR_7154))
Institut national des sciences de l'Univers (INSU - CNRS)-Université de La Réunion (UR)-Institut de Physique du Globe de Paris (IPG Paris)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)
ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)
European Project: No. 742335,ERC F-IMAGE
format Article in Journal/Newspaper
author Moreau, Ludovic
Seydoux, Léonard
Weiss, Jérôme
Campillo, Michel
author_facet Moreau, Ludovic
Seydoux, Léonard
Weiss, Jérôme
Campillo, Michel
author_sort Moreau, Ludovic
title Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
title_short Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
title_full Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
title_fullStr Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
title_full_unstemmed Analysis of microseismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring
title_sort analysis of microseismicity in sea ice with deep learning and bayesian inference: application to high-resolution thickness monitoring
publisher HAL CCSD
publishDate 2023
url https://hal.science/hal-04041306
https://hal.science/hal-04041306/document
https://hal.science/hal-04041306/file/Moreau_etal_Cryo-2023.pdf
https://doi.org/10.5194/tc-17-1327-2023
genre Arctic
Sea ice
Svalbard
The Cryosphere
genre_facet Arctic
Sea ice
Svalbard
The Cryosphere
op_source ISSN: 1994-0424
EISSN: 1994-0416
The Cryosphere
https://hal.science/hal-04041306
The Cryosphere, 2023, 17 (3), pp.1327-1341. ⟨10.5194/tc-17-1327-2023⟩
op_relation info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-17-1327-2023
info:eu-repo/grantAgreement//No. 742335/EU/Seismic Functional Imaging of the Brittle Crust/ERC F-IMAGE
hal-04041306
https://hal.science/hal-04041306
https://hal.science/hal-04041306/document
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op_doi https://doi.org/10.5194/tc-17-1327-2023
container_title The Cryosphere
container_volume 17
container_issue 3
container_start_page 1327
op_container_end_page 1341
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