Original version
Pure and Applied Geophysics (PAGEOPH). 2022, 179 (10), 3625-3645, DOI: https://doi.org/10.1007/s00024-022-03151-4
Abstract
Examining the deformation of rocks during triaxial compression may provide insights into the precursory phase that leads to large earthquakes by revealing the components of the deformation field that evolve with predictable, systematic behavior preceding catastrophic failure. Here, we build three-dimensional discrete element method simulations of the triaxial compression of rock cores that include a variety of fault geometries in order to identify the components of the deformation field, including the velocity and strain components, that enable machine learning models to predict the timing of macroscopic failure. The results suggest that the velocity field provides more valuable information about the timing of macroscopic failure than the strain field, and in particular, the velocity component parallel to the maximum compression direction. The models also strongly depend on the second invariant of the strain deviator tensor, J2