Bertrand Rouet-Leduc

Estimating the State of Faults from the Full Continuous Seismic Data Using Machine Learning

Nearly all aspects of earthquake rupture are controlled by the friction along the fault that progressively increases with tectonic forcing, but in general cannot be directly measured. Using machine learning, we show that instantaneous statistical characteristics of the seismic data are a fingerprint of the fault zone frictional state in laboratory experiments. Using a similar methodology in Earth, where we rely on other geophysical datasets as labels in order to extract informative signals from raw seismic waves, we show that subduction zones are continuously broadcasting a tremor-like signal that precisely informs of fault displacement rate throughout their slow earthquake slip cycle. We posit that this signal provides indirect, real-time access to frictional properties of megathrusts and may ultimately reveal a connection between slow slip and megaquakes