Machine Learning Reveals the State of Intermittent Frictional Dynamics in a Sheared Granular Fault

TL;DR

This paper applies machine learning to analyze velocity signals from simulated granular fault systems, revealing how statistical features can predict the fault's intermittent frictional dynamics and improve understanding of seismic processes.

Contribution

It introduces a machine learning approach that uses statistical features of particle velocity signals to predict the global state of frictional dynamics in granular fault models.

Findings

Statistical features like median and moments effectively predict fault states.

Combining signals from multiple particles enhances model accuracy.

Physical basis for error reduction is discussed.

Abstract

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.