Machine Learning Predicts the Slip Duration and Friction Drop of Laboratory Earthquakes in Sheared Granular Fault

Abstract Predicting laboratory earthquakes using machine learning has progressed markedly recently. Previous related studies mainly focus on predicting the occurrence time and shear stress of laboratory earthquakes using acoustic emission signals. Here, based on numerical simulations, we use machine learning to show that statistical features of plate motion signals contain information about the slip duration and friction drop of laboratory earthquakes. We find that the plate motion signals during the initial slip stage contain the precursor information about the slip duration of laboratory earthquakes. However, to accurately predict the friction drop, we need to incorporate the plate motion signals during the entire slip stage. The results demonstrate that the high‐order moment and variance of plate motion signals are respectively among the best predictors for the slip duration and friction drop of laboratory earthquakes. Our work provides new insights for future investigations into natural earthquake prediction through machine learning.