Title: Machine Learning Predicts Laboratory Earthquakes,
Abstract: Forecasting fault failure is a fundamental but elusive goal in earthquake
science. 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
hypothesize 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.