Small changes in seismic wave properties foretell frictional failure in laboratory experiments and in some cases on seismic faults. Such precursors include systematic changes in wave velocity and amplitude throughout the seismic cycle. However, the relationships between wave features and shear stress are complex. Here, we use data from lab friction experiments that include continuous measurement of elastic waves traversing the fault and build data-driven models to learn these complex relations. We demonstrate that deep learning models accurately predict the timing and size of laboratory earthquakes based on wave features. Additionally, the transportability of models is explored by using data from different experiments. Our deep learning models transfer well to unseen datasets providing high-fidelity models with much less training. These prediction methods can be potentially applied in the field for earthquake early warning in conjunction with long-term time-lapse seismic monitoring of crustal faults, CO2 storage sites and unconventional energy reservoirs.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)