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dc.contributor.authorSeydoux, Léonard
Balestriero, Randall
Poli, Piero
de Hoop, Maarten
Campillo, Michel
Baraniuk, Richard
dc.date.accessioned 2020-11-02T21:50:09Z
dc.date.available 2020-11-02T21:50:09Z
dc.date.issued 2020
dc.identifier.citation Seydoux, Léonard, Balestriero, Randall, Poli, Piero, et al.. "Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning." Nature Communications, 11, (2020) Springer Nature: https://doi.org/10.1038/s41467-020-17841-x.
dc.identifier.urihttps://hdl.handle.net/1911/109470
dc.description.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.
dc.language.iso eng
dc.publisher Springer Nature
dc.rights 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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
dc.type Journal article
dc.citation.journalTitle Nature Communications
dc.citation.volumeNumber 11
dc.type.dcmi Text
dc.identifier.doihttps://doi.org/10.1038/s41467-020-17841-x
dc.type.publication publisher version
dc.citation.articleNumber 3972


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