Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning

The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records.

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Autores principales:	Léonard Seydoux, Randall Balestriero, Piero Poli, Maarten de Hoop, Michel Campillo, Richard Baraniuk
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/a51cdd1b270c4f8196f7d797e88c4698
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spelling	oai:doaj.org-article:a51cdd1b270c4f8196f7d797e88c46982021-12-02T17:06:35ZClustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning10.1038/s41467-020-17841-x2041-1723https://doaj.org/article/a51cdd1b270c4f8196f7d797e88c46982020-08-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17841-xhttps://doaj.org/toc/2041-1723The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records.Léonard SeydouxRandall BalestrieroPiero PoliMaarten de HoopMichel CampilloRichard BaraniukNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Léonard Seydoux Randall Balestriero Piero Poli Maarten de Hoop Michel Campillo Richard Baraniuk Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
description	The authors here tackle the problem that too much seismic data is acquired worldwide to be evaluated in a timely fashion. Seydoux and colleagues develop a machine learning framework that can detect and cluster seismic signals in continuous seismic records.
format	article
author	Léonard Seydoux Randall Balestriero Piero Poli Maarten de Hoop Michel Campillo Richard Baraniuk
author_facet	Léonard Seydoux Randall Balestriero Piero Poli Maarten de Hoop Michel Campillo Richard Baraniuk
author_sort	Léonard Seydoux
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 Portfolio
publishDate	2020
url	https://doaj.org/article/a51cdd1b270c4f8196f7d797e88c4698
work_keys_str_mv	AT leonardseydoux clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning AT randallbalestriero clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning AT pieropoli clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning AT maartendehoop clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning AT michelcampillo clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning AT richardbaraniuk clusteringearthquakesignalsandbackgroundnoisesincontinuousseismicdatawithunsuperviseddeeplearning
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Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning

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