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Title: Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Abstract

Abstract Rocks under stress deform by creep mechanisms that include formation and slip on small‐scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self‐Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention‐based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time‐to‐failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a moremore » sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [5];  [1]
+ Show Author Affiliations
  1. Rice Univ., Houston, TX (United States)
  2. Univ. of Texas, Austin, TX (United States). Inst. for Geophysics
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  4. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Nevada, Reno, NV (United States). Dept. of Physics
  5. Pennsylvania State Univ., University Park, PA (United States). Dept. of Geosciences; Sapienza Univ. di Roma, Rome (Italy). Dipartimento Scienze della Terra
Publication Date:
Research Org.:
Rice Univ., Houston, TX (United States); Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Geothermal Technologies Office
OSTI Identifier:
1837011
Alternate Identifier(s):
OSTI ID: 1832498
Grant/Contract Number:  
SC0020345; 89233218CNA000001; SC0020512; EE0008763; DE‐SC0020345; DE‐SC0020512; DE‐EE0008763
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Solid Earth
Additional Journal Information:
Journal Volume: 126; Journal Issue: 11; Journal ID: ISSN 2169-9313
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES

Citation Formats

Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, and de Hoop, Maarten V. Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments. United States: N. p., 2021. Web. doi:10.1029/2021jb022195.
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Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, & de Hoop, Maarten V. Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments. United States. https://doi.org/10.1029/2021jb022195
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Jasperson, Hope, Bolton, David C., Johnson, Paul, Guyer, Robert, Marone, Chris, and de Hoop, Maarten V. Tue . "Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments". United States. https://doi.org/10.1029/2021jb022195. https://www.osti.gov/servlets/purl/1837011.
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@article{osti_1837011,
title = {Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments},
author = {Jasperson, Hope and Bolton, David C. and Johnson, Paul and Guyer, Robert and Marone, Chris and de Hoop, Maarten V.},
abstractNote = {Abstract Rocks under stress deform by creep mechanisms that include formation and slip on small‐scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self‐Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention‐based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time‐to‐failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.},
doi = {10.1029/2021jb022195},
journal = {Journal of Geophysical Research. Solid Earth},
number = 11,
volume = 126,
place = {United States},
year = {Tue Oct 26 00:00:00 EDT 2021},
month = {Tue Oct 26 00:00:00 EDT 2021}
}
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