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Title: Probing Slow Earthquakes With Deep Learning

Abstract

Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [1]
  1. Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA
  2. Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA, Laboratoire de Géologie, Département de Géosciences, École Normale Supérieure, PSL Research University, CNRS UMR Paris France
  3. Los Alamos National Laboratory, Geophysics Group Los Alamos NM USA, Department of Geophysics Stanford University Stanford CA USA
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1601684
Alternate Identifier(s):
OSTI ID: 1601686; OSTI ID: 1659195
Report Number(s):
LA-UR-19-27444
Journal ID: ISSN 0094-8276
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Published Article
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Name: Geophysical Research Letters Journal Volume: 47 Journal Issue: 4; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Earth Sciences

Citation Formats

Rouet‐Leduc, Bertrand, Hulbert, Claudia, McBrearty, Ian W., and Johnson, Paul A. Probing Slow Earthquakes With Deep Learning. United States: N. p., 2020. Web. doi:10.1029/2019GL085870.
Rouet‐Leduc, Bertrand, Hulbert, Claudia, McBrearty, Ian W., & Johnson, Paul A. Probing Slow Earthquakes With Deep Learning. United States. doi:https://doi.org/10.1029/2019GL085870
Rouet‐Leduc, Bertrand, Hulbert, Claudia, McBrearty, Ian W., and Johnson, Paul A. Mon . "Probing Slow Earthquakes With Deep Learning". United States. doi:https://doi.org/10.1029/2019GL085870.
@article{osti_1601684,
title = {Probing Slow Earthquakes With Deep Learning},
author = {Rouet‐Leduc, Bertrand and Hulbert, Claudia and McBrearty, Ian W. and Johnson, Paul A.},
abstractNote = {Slow earthquakes may trigger failure on neighboring locked faults that are stressed sufficiently to break, and slow slip patterns may evolve before a nearby great earthquake. However, even in the clearest cases such as Cascadia, slow earthquakes and associated tremor have only been observed in intermittent and discrete bursts. By training a convolutional neural network to detect known tremor on a single seismic station in Cascadia, we isolate and identify tremor and slip preceding and following known larger slow events. The deep neural network can be used for the detection of quasi-continuous tremor, providing a proxy that quantifies the slow slip rate. Furthermore, the model trained in Cascadia recognizes tremor in other subduction zones and also along the San Andreas Fault at Parkfield, suggesting a universality of waveform characteristics and source processes, as posited from experiments and theory.},
doi = {10.1029/2019GL085870},
journal = {Geophysical Research Letters},
number = 4,
volume = 47,
place = {United States},
year = {2020},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1029/2019GL085870

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