What is it about?
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.
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Why is it important?
Whether ML approaches applied to continuous seismic or other geophysical data succeed in providing information on timing of earthquakes (not to mention the challenge of predicting earthquake magnitude), this approach may reveal unidentified signals associated with undiscovered fault physics. Furthermore, this method may be useful for failure prediction in a broad spectrum of industrial and natural materials. Technology is at a confluence of dramatic advances in instrumentation, machine learning, the ability to handle massive data sets and faster computers. Thus, the stage has been set for potentially marked advances in earthquake science.
Perspectives
We were able to build a model that accurately estimates the frictional state of a laboratory fault and the time remaining before failure. Our model captures the physical state of the fault using new signals in the seismic data, that were thought to be noise. Our approach extends to different experimental conditions, and to the analysis of different materials: glass beads, quartz powder, and bare granite. Our approach is simple to implement and computationally inexpensive. We can process large amounts of seismic data on a single CPU. Many different systems remain to be explored: simulations, other laboratory experiments, and real faults (tectonic forcing, induced seismicity).
Bertrand Rouet-Leduc
Los Alamos National Laboratory
Read the Original
This page is a summary of: Machine Learning Predicts Laboratory Earthquakes, Geophysical Research Letters, September 2017, Wiley,
DOI: 10.1002/2017gl074677.
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