Fault systems monitoring using machine learning

Abstract

Traditional seismology methods are designed to build catalogs of seismic events that are obviously different from the remaining seismic data, even to the naked eye, and discard more than 99% of the data. Los Alamos National Lab researchers developed machine learning methods to examine massive amounts of raw seismic data. The scientists found that the continuous data discarded by cataloging methods are informative of the physical state of faults in theoretical models, in laboratory experiments, and in the field. This information may enable the detection of predictive markers of seismic events and the provide insights into the underlying physics to support earthquake hazard assessment.