RuptureNet2D, a Deep Neural Network Based Surrogate for Dynamic Earthquake Rupture Simulation in Two Dimensions

Abstract Earthquake dynamic rupture simulations are crucial for physics‐based seismic hazard assessment. However, due to its intricate dynamics spanning vast spatial and temporal scales, these simulations pose a complex and computationally challenging problem. Here, we propose an end‐to‐end deep learning model (RuptureNet2D) as a cost‐effective alternative (at evaluation time) to expensive numerical simulations. This model is trained on a data set of 300k simulations in two dimensions and is capable of simultaneously predicting two key earthquake source parameters: rupture time and final slip. Testing reveals exceptional model performance on faults with homogeneous and heterogeneous (with one or two asperities) frictional parameters but only requires a fraction (1/100,000 to 1/1,000) of the prediction time compared to numerical simulations. However, the accuracy of the neural network decreases as the fault length and number of asperities increases beyond the range of the training data. Nevertheless, our work demonstrates the applicability and efficiency of neural networks as a surrogate for earthquake dynamic rupture simulations which has a potential of significantly accelerating physics‐based earthquake source inversion and advancing our understanding of earthquake physics.