MACHINE-LEARNING EARTHQUAKE PREDICTION.

• Position: COMPUTER SCIENCE

By listening to the acoustic signal emitted by a laboratory-created earthquake, a computer-science approach using machine learning can predict the time remaining before the fault fails. "At any given instant, the noise coming from the lab fault zone provides quantitative information on when the fault will slip," says Paul Johnson, a fellow at Los Alamos (N.M.) National Laboratory and lead investigator on research published in Geophysical Research Letters.

"The novelty of our work is the use of machine learning to discover and understand new physics of failure, through examination of the recorded auditory signal from the experimental setup. I think the future of earthquake physics will rely heavily on machine learning to process massive amounts of raw seismic data. Our work represents an Important step in this direction."

Not only does the work have potential significance to earthquake forecasting, but the approach is far-reaching, applicable to potentially all failure scenarios, including nondestructive testing of industrial materials brittle failure of all kinds, avalanches, and other events.

Machine-learning is an artificial-intelligence approach to allowing the computer to learn from new data...