Earthquake Rupture Dynamics From Graph Neural Networks

Abstract Earthquakes arise from tectonic loading of complex fault systems consisting of heterogeneous material parameters, geometry, rheology, and prestress. All of those are subject to uncertainties, and their interactions and sensitivities for the dynamic rupture problem are incompletely understood. Here, we apply Graph Neural Networks (GNNs) to approximate the behavior learned from more computationally intensive, physics‐based (“high‐fidelity”) computations to build a GNN‐based simulator (GNS) for earthquake rupture dynamics. Given only a minimum input –the hypocenter location– our GNS can reproduce rate‐weakening friction governed dynamic rupture behavior, from nucleation to propagation and termination. Outside the training set, the GNS can generalize well to different hypocenter locations, fault sizes, and pre‐stress state levels while achieving a factor ∼20–40 per‐time‐step computational speedup. This may allow for more efficient estimates of the mapping from pre‐earthquake state, as might be inferred from geodesy, to expected rupture dynamics, for example. By extracting a coarse‐grained version of the underlying dynamics, the GNS provides new perspectives to explore the physics of rupture. Further development of GNS may enable new kinds of parameter space exploration and provide surrogates for Bayesian model inference, uncertainty quantification, and optimal experimental design.