Machine learning bridges microslips and slip avalanches of sheared granular gouge

Understanding the origin of stress avalanche of fault
gouges may offer deeper insights into many geophysical processes such as
earthquakes. Microslips of sheared granular gouges were found to be precursors
of large slip events, but the documented relation between local and global
avalanches remains largely qualitative. We examine the stick-slip behavior of a slowly sheared granular system using
discete element method simulations. The microslips,
i.e., local avalanche events, are found to demonstrate significantly different
statistical and spatial characteristics between the stick and slip states. We further
investigate the correlation between the global stress fluctuations and the
features extracted from microslips based on the machine learning (ML) approach.
The data-driven model that incorporates the information of the spatial
distribution of microslips can robustly predict the magnitude of stress
fluctuation. A further feature importance analysis confirms
that the spatial patterns of microslips manifest key
information governing the global stress fluctuations.