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

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: This is version 3 of this Preprint.


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Christopher X. Ren, Omid Dorostkar, Bertrand Rouet‐Leduc , Claudia Hulbert , Dominik Strebel, Robert Guyer, Paul A. Johnson, Jan Carmeliet


The seismogenic plate boundaries are presumed to behave similarly 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.



Civil and Environmental Engineering, Earth Sciences, Engineering, Geophysics and Seismology, Geotechnical Engineering, Physical Sciences and Mathematics


machine learning, friction, Fault mechanics, Stick-slip, granular materials, DEM


Published: 2019-03-05 13:21

Last Updated: 2019-04-09 13:10

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Academic Free License (AFL) 3.0

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