Earthquake fault slip and nonlinear dynamics

Abstract

Earthquake fault slip under shear forcing can be envisioned as a nonlinear dynamical process dominated by a single slip plane. In contrast, nonlinear behavior in Earth materials (e.g., rock) is driven by a strain-induced ensemble activation and slip of a large number of distributed features—cracks and grain boundary slip across many scales in the volume. The bulk recovery of a fault post-failure and that of a rock sample post dynamic or static forcing (”aging” or the “slow dynamics”) is very similar with approximate log(time) dependence for much of the recovery. In our work, we analyze large amounts of continuous acoustic emission (AE) data from a laboratory “earthquake machine,” applying machine learning, with the task of determining what information regarding fault slip the AE signal may carry. Applying the continuous AE as input to machine learning models and using measured fault friction, displacement, etc., as model labels, we find that the AE are imprinted with information regarding the fault friction and displacement. We are currently developing approaches to probe stick-slip on Earth faults, those that are responsible for damaging earthquakes. A related goal is to quantitatively relate nonlinear elastic theory (e.g., PM space, Arrhenius) to frictional theory (e.g., rate-state).