  Seismogenic plate boundaries are presumed to behave in a similar manner to a
densely packed granular medium, where fault and blocks systems rapidly
rearrange the distribution of forces within themselves, as particles do in
slowly sheared granular systems. We use machine learning and show that
statistical features of velocity signals from individual particles in a
simulated sheared granular fault contain information regarding the
instantaneous global state of intermittent frictional stick-slip dynamics. We
demonstrate that combining features built from the signals of more particles
can improve the accuracy of the global model, and discuss the physical basis
behind decrease in error. We show that the statistical features such as median
and higher moments of the signals that represent the particle displacement in
the direction of shearing are among the best predictive features. Our work
provides novel insights into the applications of machine learning in studying
frictional processes that take place in geophysical systems.
