TY - JOUR
T1 - Clustering earthquake signals and background noises in continuous seismic data with unsupervised deep learning
AU - Seydoux, Léonard
AU - Balestriero, Randall
AU - Poli, Piero
AU - Hoop, Maarten de
AU - Campillo, Michel
AU - Baraniuk, Richard
N1 - Funding Information:
L.S., P.P., and M.C. acknowledge support from the European Research Council under the European Union Horizon 2020 research and innovation program (grant agreement no. 742335, F-IMAGE). M.C. and L.S. acknowledge the support of the Multidisciplinary Institute in Artificial Intelligence MIAI@Grenoble Alpes (Program “Investissements d’avenir” contract ANR-19-P3IA-0003, France). M.V.d.H. gratefully acknowledges support from the Simons Foundation under the MATH + X program and from DOE under grant DE-SC0020345. R.B. and R.G.B. were supported by NSF grants IIS-17-30574 and IIS-18-38177, AFOSR grant FA9550-18-1-0478, ONR grant N00014-18-12571, and a DOD Vannevar Bush Faculty Fellowship, ONR grant N00014-18-1-2047. L.S. thanks Romain Cosentino for very helpful discussions and comments.
Funding Information:
The facilities of IRIS Data Services, and specifically the IRIS Data Management Center, were used for access to waveforms and related metadata used in this study. IRIS Data Services are funded through the Seismological Facilities for the Advancement of Geoscience and EarthScope (SAGE) Project funded by the NSF under Cooperative Agreement EAR-1261681. The maps were made with the Cartopy Python library (v0.11.2. 22-Aug-2014. Met Office.). The topographic models were downloaded from the Global Multi-Resolution Topography databse at https://www.gmrt.org.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
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U2 - 10.1038/s41467-020-17841-x
DO - 10.1038/s41467-020-17841-x
M3 - Article
C2 - 32769972
AN - SCOPUS:85089179854
VL - 11
JO - Nat Commun
JF - Nat Commun
SN - 2041-1723
IS - 1
M1 - 3972
ER -