Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
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 b...
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ftpubmed:oai:pubmedcentral.nih.gov:7414231 2023-05-15T16:28:55+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 2020-08-07 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414231/ http://www.ncbi.nlm.nih.gov/pubmed/32769972 https://doi.org/10.1038/s41467-020-17841-x en eng Nature Publishing Group UK http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414231/ http://www.ncbi.nlm.nih.gov/pubmed/32769972 http://dx.doi.org/10.1038/s41467-020-17841-x © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. CC-BY Nat Commun Article Text 2020 ftpubmed https://doi.org/10.1038/s41467-020-17841-x 2020-08-23T00:23:40Z 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. Text Greenland Nuugaatsiaq PubMed Central (PMC) Greenland Nuugaatsiaq ENVELOPE(-53.212,-53.212,71.536,71.536) Nature Communications 11 1 |
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Article 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 |
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Article |
description |
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. |
format |
Text |
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 |
Nature Publishing Group UK |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414231/ http://www.ncbi.nlm.nih.gov/pubmed/32769972 https://doi.org/10.1038/s41467-020-17841-x |
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Greenland Nuugaatsiaq |
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Greenland Nuugaatsiaq |
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Greenland Nuugaatsiaq |
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Greenland Nuugaatsiaq |
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Nat Commun |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414231/ http://www.ncbi.nlm.nih.gov/pubmed/32769972 http://dx.doi.org/10.1038/s41467-020-17841-x |
op_rights |
© The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
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CC-BY |
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https://doi.org/10.1038/s41467-020-17841-x |
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Nature Communications |
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11 |
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