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:
-
[1];
[2];
[1];
[1];
[3];
[1];
[1];
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Univ. of Oxford (United Kingdom); Federal Inst. of Technology, Zurich (Switzerland)
- Swiss Federal Lab. for Materials Science and Technology (Empa), Dübendorf (Switzerland)
- 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:https://doi.org/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:https://doi.org/10.1029/2019GL082706. https://www.osti.gov/servlets/purl/1544741.
@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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Works referencing / citing this record:
Machine Learning Reveals the Seismic Signature of Eruptive Behavior at Piton de la Fournaise Volcano
journal, February 2020
- Ren, C. X.; Peltier, A.; Ferrazzini, V.
- Geophysical Research Letters, Vol. 47, Issue 3
Grain Friction Controls Characteristics of Seismic Cycle in Faults With Granular Gouge
journal, July 2019
- Dorostkar, Omid; Carmeliet, Jan
- Journal of Geophysical Research: Solid Earth, Vol. 124, Issue 7