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Attention Network Forecasts Time‐to‐Failure in Laboratory Shear Experiments

Year: 2021
Type: article
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
Authors: Hope Jasperson, David C. Bolton, Paul Johnson, Robert Guyer, Chris Marone, Maarten V. de Hoop
Institutions: Rice University, University of Texas Institute for Geophysics, The University of Texas at Austin, Los Alamos National Laboratory, University of Nevada, Reno, Pennsylvania State University, Sapienza University of Rome
Language: English
Open Access status: bronze
Awards: G1017065088, G1527848044, G2445961536, DEEP LEARNING FOR FORECASTING OF FRACTURE AND FAULT EVOLUTION, The physics of Earthquake faulting: learning from laboratory earthquake prediCTiON to Improve forecasts of the spectrum of tectoniC failure modes: TECTONIC, MACHINE LEARNING AND DATA SCIENCE TO ADVANCE LABORATORY EARTHQUAKE PREDICTION AND ILLUMINATE THE MECHANICS OF PRECURSORS TO FAILURE, G3137353516, G7663372167, MACHINE LEARNING APPROACHES TO PREDICTING INDUCED SEISMICITY AND IMAGING GEOTHERMAL RESERVOIR PROPERTIES
OpenAlex ID: W3150316474
FWCI: 1.649
Cites: 89
Cited by: 29
Related to: 20