Machine Learning Predicts Laboratory Earthquakes

Rouet-Leduc, Bertrand; Hulbert, Claudia; Lubbers, Nicholas; Barros, Kipton; Humphreys, Colin J.; Johnson, Paul Allan

doi:10.1002/2017GL074677

Title: Machine Learning Predicts Laboratory Earthquakes

Journal Article · Wed Aug 30 00:00:00 EDT 2017 · Geophysical Research Letters

DOI:https://doi.org/10.1002/2017GL074677· OSTI ID:1460625

Rouet-Leduc, Bertrand ^[1]; Hulbert, Claudia ^[2]; Lubbers, Nicholas ^[3]; Barros, Kipton ^[2]; Humphreys, Colin J. ^[4];

^[2]

Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of Cambridge, Cambridge (United Kingdom)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Boston Univ., Boston, MA (United States)
Univ. of Cambridge, Cambridge (United Kingdom)

Here, we apply machine learning to data sets from shear laboratory experiments, with the goal of identifying hidden signals that precede earthquakes. Here we show that by listening to the acoustic signal emitted by a laboratory fault, machine learning can predict the time remaining before it fails with great accuracy. These predictions are based solely on the instantaneous physical characteristics of the acoustical signal and do not make use of its history. Surprisingly, machine learning identifies a signal emitted from the fault zone previously thought to be low-amplitude noise that enables failure forecasting throughout the laboratory quake cycle. We infer that this signal originates from continuous grain motions of the fault gouge as the fault blocks displace. We posit that applying this approach to continuous seismic data may lead to significant advances in identifying currently unknown signals, in providing new insights into fault physics, and in placing bounds on fault failure times.

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Research Organization:: Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1460625

Report Number(s):: LA-UR-16-26108

Journal Information:: Geophysical Research Letters, Vol. 44, Issue 18; ISSN 0094-8276

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 173 works

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Title: Machine Learning Predicts Laboratory Earthquakes

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