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

Léonard Seydoux (), Randall Balestriero, Piero Poli, Maarten de Hoop, Michel Campillo and Richard Baraniuk
Additional contact information
Léonard Seydoux: Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine
Randall Balestriero: Rice University, 6100 Main MS-134
Piero Poli: Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine
Maarten de Hoop: Rice University, 6100 Main MS-134
Michel Campillo: Université Grenoble-Alpes, UMR CNRS 5375, 1381 Rue de la Piscine
Richard Baraniuk: Rice University, 6100 Main MS-134

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.nature.com/articles/s41467-020-17841-x Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17841-x

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-17841-x

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().