Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
<jats:title>Abstract</jats:title><jats:p>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 conduct...
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ftosti:oai:osti.gov:1845148 2023-07-30T04:03:52+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 2023-07-04 application/pdf http://www.osti.gov/servlets/purl/1845148 https://www.osti.gov/biblio/1845148 https://doi.org/10.1038/s41467-020-17841-x unknown http://www.osti.gov/servlets/purl/1845148 https://www.osti.gov/biblio/1845148 https://doi.org/10.1038/s41467-020-17841-x doi:10.1038/s41467-020-17841-x 59 BASIC BIOLOGICAL SCIENCES 2023 ftosti https://doi.org/10.1038/s41467-020-17841-x 2023-07-11T10:10:09Z <jats:title>Abstract</jats:title><jats:p>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.</jats:p> Other/Unknown Material Greenland Nuugaatsiaq SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Greenland Nuugaatsiaq ENVELOPE(-53.212,-53.212,71.536,71.536) Nature Communications 11 1 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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59 BASIC BIOLOGICAL SCIENCES |
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59 BASIC BIOLOGICAL 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 |
59 BASIC BIOLOGICAL SCIENCES |
description |
<jats:title>Abstract</jats:title><jats:p>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.</jats:p> |
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 |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1845148 https://www.osti.gov/biblio/1845148 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_relation |
http://www.osti.gov/servlets/purl/1845148 https://www.osti.gov/biblio/1845148 https://doi.org/10.1038/s41467-020-17841-x doi:10.1038/s41467-020-17841-x |
op_doi |
https://doi.org/10.1038/s41467-020-17841-x |
container_title |
Nature Communications |
container_volume |
11 |
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1 |
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1772814996193935360 |