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

• Published in: Nature Communications
• Main Authors: Seydoux, Léonard, Balestriero, Randall, Poli, Piero, Hoop, Maarten de, Campillo, Michel, Baraniuk, Richard
• Other Authors: Institut des Sciences de la Terre (ISTerre), Institut national des sciences de l'Univers (INSU - CNRS)-Institut de recherche pour le développement IRD : UR219-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)-Université Gustave Eiffel-Université Grenoble Alpes (UGA), Electrical and Computer Engineering - Rice University, Rice University Houston, ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019), European Project: 789742335,F-IMAGE
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2020
• Subjects: [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph]; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; Greenland; Nuugaatsiaq
• Online Access: https://hal.univ-grenoble-alpes.fr/hal-03411505; https://hal.univ-grenoble-alpes.fr/hal-03411505/document; https://hal.univ-grenoble-alpes.fr/hal-03411505/file/Preprint.pdf; https://doi.org/10.1038/s41467-020-17841-x

International audience 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 sta...