Probing Earthquake Fault Slip using Machine Learning

Earthquakes take place when two juxtaposed fault blocks are stressed sufficiently to overcome the frictional force holding them in place and they abruptly slip relative to each other. Earthquake faults exhibit a continuum of behaviors ranging from stick slip associated with strong shaking, to slow slip which is primarily aseismic, to very slow slip that is both aseismic and can take place over hours to months. We are characterizing faulting physics by analyzing with machine learning continuous acoustic data streams in the laboratory and continuous seismic data streams in Earth. We use as labels characteristics of the measured fault slip behavior in the laboratory such as the fault friction, shear displacement and fault thickness. In Earth, we use surface displacement as determined by Global Positioning Systems (GPS). Other data data such as INSAR can be used as well. We find that the laboratory acoustic data and the Earth seismic data are a type of Rosetta Stone revealing fault characteristics at all times and fault displacements. This is a surprising observation because previously we believed most or much of the signal was noise. Here we describe an overview of recent work in this area and also describe recent efforts on parallel problems such as volcanoes and geysers.