Machine Learning Predicts Laboratory Earthquakes

By Bertrand Rouet-Leduc, Claudia Hulbert, Nicholas Lubbers, Kipton Barros, Colin J. Humphreys, P. Johnson. Geophysical Research Letters, 2017

349 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Geophysics

View Paper: https://www.semanticscholar.org/paper/2f6000c3be00335633cd490f03d642fcf101cd0d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Gregory C. Beroza
2. Peter Gerstoft
3. P. Johnson
4. Maarten V. de Hoop
5. Karianne J. Bergen
6. Zachary E. Ross
7. S. M. Mousavi
8. Weiqiang Zhu

In Geophysics

1. Gregory C. Beroza
2. Karianne J. Bergen
3. P. Johnson
4. Peter Gerstoft
5. Maarten V. de Hoop
6. Zachary E. Ross
7. S. M. Mousavi
8. Weiqiang Zhu

Citations in the corpus

• Machine learning for data-driven discovery in solid Earth geoscience (2019, background): “The approach identified signals that were deemed noise beforehand ( 28 ).”
• Machine Learning in Seismology: Turning Data into Insights (2018, methodology): “A recent example is using ML to predict the timing of the next slip event in laboratory slip experiments with features extracted from low-amplitude acoustic emissions that were previously considered to be noise (Rouet-Leduc et al. , 2017).”