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Over the last two decades, strain and GPS measurements have shown that slowslip on earthquake faults is a widespread phenomenon. Slow slip is alsoinferred from correlated small amplitude seismic signals known as nonvolcanictremor and low frequency earthquakes (LFEs). Slow slip has been reproduced inlaboratory and simulation studies, however the fundamental physics of thesephenomena and their relationship to dynamic earthquake rupture remains poorlyunderstood. Here we show that, in a laboratory setting, continuous seismicwaves are imprinted with fundamental signatures of the fault's physical state.Using machine learning on continuous seismic waves, we can infer several bulkcharacteristics of the fault (friction, shear displacement, gouge thickness),at any time during the slow slip cycle. This analysis also allows us to infermany properties of the future behavior of the fault, including the timeremaining before the next slow slip event. Our work suggests that by applyingmachine learning approaches to continuous seismic data, new insight into thephysics of slow slip could be obtained in Earth.

Estimating the Physical State of a Laboratory Slow Slipping Fault from Seismic Signals