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Title: Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

Abstract

Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [1]; ORCiD logo [1];  [4]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Univ. of Oxford (United Kingdom); Federal Inst. of Technology, Zurich (Switzerland)
  3. Swiss Federal Lab. for Materials Science and Technology (Empa), Dübendorf (Switzerland)
  4. Federal Inst. of Technology, Zurich (Switzerland)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Chemical Sciences, Geosciences & Biosciences Division
OSTI Identifier:
1544741
Report Number(s):
[LA-UR-19-22300]
[Journal ID: ISSN 0094-8276]
Grant/Contract Number:  
[89233218CNA000001]
Resource Type:
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
[ Journal Volume: 46; Journal Issue: 13]; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 97 MATHEMATICS AND COMPUTING; Machine learning; fault gouge; simulation; DEM; friction; stick-slip

Citation Formats

Ren, Christopher Xiang, Dorostkar, Omid, Rouet‐Leduc, Bertrand Philippe, Hulbert, Claudia L., Strebel, Dominik, Guyer, Robert A., Johnson, Paul Allan, and Carmeliet, Jan. Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault. United States: N. p., 2019. Web. doi:10.1029/2019GL082706.
Ren, Christopher Xiang, Dorostkar, Omid, Rouet‐Leduc, Bertrand Philippe, Hulbert, Claudia L., Strebel, Dominik, Guyer, Robert A., Johnson, Paul Allan, & Carmeliet, Jan. Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault. United States. doi:10.1029/2019GL082706.
Ren, Christopher Xiang, Dorostkar, Omid, Rouet‐Leduc, Bertrand Philippe, Hulbert, Claudia L., Strebel, Dominik, Guyer, Robert A., Johnson, Paul Allan, and Carmeliet, Jan. Mon . "Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault". United States. doi:10.1029/2019GL082706.
@article{osti_1544741,
title = {Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault},
author = {Ren, Christopher Xiang and Dorostkar, Omid and Rouet‐Leduc, Bertrand Philippe and Hulbert, Claudia L. and Strebel, Dominik and Guyer, Robert A. and Johnson, Paul Allan and Carmeliet, Jan},
abstractNote = {Seismogenic plate boundaries are posited to behave in a similar manner to a densely packed granular medium, where fault and block systems rapidly rearrange the distribution of forces within themselves, as particles do in slowly sheared granular systems. Machine learning is used to show that statistical features of velocity signals from individual particles in a simulated sheared granular fault contain information regarding the instantaneous global state of intermittent frictional stick-slip dynamics. We demonstrate that combining features built from the signals of more particles can improve the accuracy of the global model and discuss the physical basis behind the decrease in error. We show that the statistical features such as median and higher moments of the signals that represent the particle displacement in the direction of shearing are among the best predictive features. We demonstrate here novel insights into the applications of machine learning in studying frictional processes occurring in geophysical systems.},
doi = {10.1029/2019GL082706},
journal = {Geophysical Research Letters},
number = [13],
volume = [46],
place = {United States},
year = {2019},
month = {6}
}

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