Machine Learning Predicts Laboratory Earthquakes

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. Plain Language Summary Predicting the timing and magnitude of an earthquake is a fundamental goal of geoscientists. In a laboratory setting, we show we can predict “labquakes” by applying new developments in machine learning (ML), which exploits computer programs that expand and revise themselves based on new data. We use ML to identify telltale sounds—much like a squeaky door—that predict when a quake will occur. The experiment closely mimics Earth faulting, so the same approach may work in predicting timing, but not size, of an earthquake. This approach could be applied to predict avalanches, landslides, failure of machine parts, and more. Key Points Machine learning appears to discern the frictional state when applied to laboratory seismic data recorded during a shear experiment Machine learning uses statistical characteristics of the recorded seismic signal to accurately predict slip failure time We posit that similar machine learning approaches applied to geophysical data in Earth will provide insight in fault frictional processes