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

doi:10.1038/s41467-020-17841-x

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

Journal Article · Fri Aug 07 00:00:00 EDT 2020 · Nature Communications

DOI:https://doi.org/10.1038/s41467-020-17841-x· OSTI ID:1803960

^[1];

^[2]; Poli, Piero ^[1]; Hoop, Maarten de ^[3]; Campillo, Michel ^[1]; Baraniuk, Richard ^[4]

Univ. of Grenoble Alpes, Saint-Martin-d-Heres (France). ISTerre, équipe Ondes et Structures
Rice Univ., Houston, TX (United States). Electrical and Computational Engineering
Rice Univ., Houston, TX (United States). Computational and Applied Mathematics
Rice Univ., Houston, TX (United States). Electrical and Computational Engineering

AbstractThe 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.

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Research Organization:: Rice Univ., Houston, TX (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES). Chemical Sciences, Geosciences & Biosciences Division

Grant/Contract Number:: SC0020345

OSTI ID:: 1803960

Alternate ID(s):: OSTI ID: 1845148

Journal Information:: Nature Communications, Vol. 11, Issue 1; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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