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Title: Estimating Fault Friction From Seismic Signals in the Laboratory

Abstract

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. Finally, these results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [2];  [3];  [4]; ORCiD logo [2];  [5]; ORCiD logo [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Pennsylvania State Univ., University Park, PA (United States). Dept. of Geosciences
  3. Univ. of Cambridge (United Kingdom). Dept. of Materials Science and Metallurgy
  4. Pennsylvania State Univ., University Park, PA (United States). Dept. of Geosciences; Grenoble Alpes Univ., Grenoble (France). Inst. of Earth Sciences (ISTerre)
  5. Los Alamos National Laboratory, Geophysics Group, Los Alamos NM USA
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1440476
Alternate Identifier(s):
OSTI ID: 1558055
Report Number(s):
LA-UR-17-29312; LA-UR-18-29849
Journal ID: ISSN 0094-8276
Grant/Contract Number:  
AC52-06NA25396; 89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Geophysical Research Letters
Additional Journal Information:
Journal Volume: 45; Journal Issue: 3; Journal ID: ISSN 0094-8276
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; machine learning; seismic signal identification; laboratory earthquake; fault friction; earthquake hazard; Earth Sciences

Citation Formats

Rouet-Leduc, Bertrand, Hulbert, Claudia, Bolton, David C., Ren, Christopher X., Riviere, Jacques, Marone, Chris, Guyer, Robert A., and Johnson, Paul A. Estimating Fault Friction From Seismic Signals in the Laboratory. United States: N. p., 2018. Web. doi:10.1002/2017GL076708.
Rouet-Leduc, Bertrand, Hulbert, Claudia, Bolton, David C., Ren, Christopher X., Riviere, Jacques, Marone, Chris, Guyer, Robert A., & Johnson, Paul A. Estimating Fault Friction From Seismic Signals in the Laboratory. United States. doi:10.1002/2017GL076708.
Rouet-Leduc, Bertrand, Hulbert, Claudia, Bolton, David C., Ren, Christopher X., Riviere, Jacques, Marone, Chris, Guyer, Robert A., and Johnson, Paul A. Mon . "Estimating Fault Friction From Seismic Signals in the Laboratory". United States. doi:10.1002/2017GL076708. https://www.osti.gov/servlets/purl/1440476.
@article{osti_1440476,
title = {Estimating Fault Friction From Seismic Signals in the Laboratory},
author = {Rouet-Leduc, Bertrand and Hulbert, Claudia and Bolton, David C. and Ren, Christopher X. and Riviere, Jacques and Marone, Chris and Guyer, Robert A. and Johnson, Paul A.},
abstractNote = {Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing but in general cannot be directly measured. We show that fault friction can be determined at any time, from the continuous seismic signal. In a classic laboratory experiment of repeating earthquakes, we find that the seismic signal follows a specific pattern with respect to fault friction, allowing us to determine the fault's position within its failure cycle. Using machine learning, we show that instantaneous statistical characteristics of the seismic signal are a fingerprint of the fault zone shear stress and frictional state. Further analysis of this fingerprint leads to a simple equation of state quantitatively relating the seismic signal power and the friction on the fault. Finally, these results show that fault zone frictional characteristics and the state of stress in the surroundings of the fault can be inferred from seismic waves, at least in the laboratory.},
doi = {10.1002/2017GL076708},
journal = {Geophysical Research Letters},
number = 3,
volume = 45,
place = {United States},
year = {2018},
month = {1}
}

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