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

• Published in: Nature Communications
• Main Authors: Seydoux, Léonard, Balestriero, Randall Rice Univ., Houston, TX . Electrical and Computational Engineering, Poli, Piero Univ. of Grenoble Alpes, Saint-Martin-d-Heres . ISTerre, équipe Ondes et Structures, Hoop, Maarten de Rice Univ., Houston, TX . Computational and Applied Mathematics, Campillo, Michel Univ. of Grenoble Alpes, Saint-Martin-d-Heres . ISTerre, équipe Ondes et Structures, Baraniuk, Richard Rice Univ., Houston, TX . Electrical and Computational Engineering
• Language: unknown
• Published: 2023
• Subjects: 59 BASIC BIOLOGICAL SCIENCES; Greenland; Nuugaatsiaq
• Online Access: http://www.osti.gov/servlets/purl/1803960; https://www.osti.gov/biblio/1803960; https://doi.org/10.1038/s41467-020-17841-x

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.