Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
International audience Abstract The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased b...
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ftccsdartic:oai:HAL:hal-03430024v1 2023-05-15T16:29:05+02:00 Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning Seydoux, Léonard Balestriero, Randall Poli, Piero Hoop, Maarten de Campillo, Michel Baraniuk, Richard Centre National de la Recherche Scientifique (CNRS) 2020-12 https://hal.archives-ouvertes.fr/hal-03430024 https://doi.org/10.1038/s41467-020-17841-x en eng HAL CCSD Nature Publishing Group info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-17841-x hal-03430024 https://hal.archives-ouvertes.fr/hal-03430024 doi:10.1038/s41467-020-17841-x ISSN: 2041-1723 EISSN: 2041-1723 Nature Communications https://hal.archives-ouvertes.fr/hal-03430024 Nature Communications, Nature Publishing Group, 2020, 11 (1), ⟨10.1038/s41467-020-17841-x⟩ [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2020 ftccsdartic https://doi.org/10.1038/s41467-020-17841-x 2021-11-20T23:26:09Z International audience Abstract The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas. Article in Journal/Newspaper Greenland Nuugaatsiaq Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) Greenland Nuugaatsiaq ENVELOPE(-53.212,-53.212,71.536,71.536) Nature Communications 11 1 |
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Archive ouverte HAL (Hyper Article en Ligne, CCSD - Centre pour la Communication Scientifique Directe) |
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English |
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[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
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[SDU.STU]Sciences of the Universe [physics]/Earth Sciences Seydoux, Léonard Balestriero, Randall Poli, Piero Hoop, Maarten de Campillo, Michel Baraniuk, Richard Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
topic_facet |
[SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience Abstract The continuously growing amount of seismic data collected worldwide is outpacing our abilities for analysis, since to date, such datasets have been analyzed in a human-expert-intensive, supervised fashion. Moreover, analyses that are conducted can be strongly biased by the standard models employed by seismologists. In response to both of these challenges, we develop a new unsupervised machine learning framework for detecting and clustering seismic signals in continuous seismic records. Our approach combines a deep scattering network and a Gaussian mixture model to cluster seismic signal segments and detect novel structures. To illustrate the power of the framework, we analyze seismic data acquired during the June 2017 Nuugaatsiaq, Greenland landslide. We demonstrate the blind detection and recovery of the repeating precursory seismicity that was recorded before the main landslide rupture, which suggests that our approach could lead to more informative forecasting of the seismic activity in seismogenic areas. |
author2 |
Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Seydoux, Léonard Balestriero, Randall Poli, Piero Hoop, Maarten de Campillo, Michel Baraniuk, Richard |
author_facet |
Seydoux, Léonard Balestriero, Randall Poli, Piero Hoop, Maarten de Campillo, Michel Baraniuk, Richard |
author_sort |
Seydoux, Léonard |
title |
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
title_short |
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
title_full |
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
title_fullStr |
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
title_full_unstemmed |
Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
title_sort |
clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning |
publisher |
HAL CCSD |
publishDate |
2020 |
url |
https://hal.archives-ouvertes.fr/hal-03430024 https://doi.org/10.1038/s41467-020-17841-x |
long_lat |
ENVELOPE(-53.212,-53.212,71.536,71.536) |
geographic |
Greenland Nuugaatsiaq |
geographic_facet |
Greenland Nuugaatsiaq |
genre |
Greenland Nuugaatsiaq |
genre_facet |
Greenland Nuugaatsiaq |
op_source |
ISSN: 2041-1723 EISSN: 2041-1723 Nature Communications https://hal.archives-ouvertes.fr/hal-03430024 Nature Communications, Nature Publishing Group, 2020, 11 (1), ⟨10.1038/s41467-020-17841-x⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-020-17841-x hal-03430024 https://hal.archives-ouvertes.fr/hal-03430024 doi:10.1038/s41467-020-17841-x |
op_doi |
https://doi.org/10.1038/s41467-020-17841-x |
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Nature Communications |
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11 |
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1 |
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1766018765273694208 |