Rupture Propagation Direction at the Junction of the Garlock and San Andreas Fault System: A Machine Learning Classification Approach Driven by Earthquake Simulations

Abstract

Anticipating potential earthquake rupture paths in the San Andreas fault system poses considerable challenges due to its complex geometry, stress conditions, and frictional properties. A commonly used approach relies on independently varying parameters through iterations of earthquake simulations to better understand the effects of such parameters on rupture. However, such approaches are computationally expensive. In this study we implement a machine learning algorithm trained on outputs from the Rate-and-State earthquake simulator (RSQSim) to understand the importance of various parameters on the rupture path at the branch intersection of San Andreas and Garlock fault. We show how machine learning models employing gradient boosting (xgboost) and Random Forest trained on a data set of ∼9,800 simulated earthquakes can capture the rupture classification as well as extract feature importance of fault parameters. We demonstrate that for certain scenarios the machine learning models have considerable testing accuracy in classifying the rupture path, and augmentation of features in the input training data leads to improvements in the accuracy both in terms of precision and recall. Furthermore, our machine learning models suggest that the pre-earthquake conditions of the fault on which the rupture nucleated are the dominant parameters which affect rupture path at the branch intersection. This approach stands as a promising tool for rupture propagation and fault parameter studies, particularly for understanding which parameters are crucial for determining branching behavior. Consequently, it can help interpret the behavior of both modeled and real world faults.