Fourier Neural Operators for Accelerating Earthquake Dynamic Rupture Simulations

ABSTRACT Dynamic rupture modeling plays a crucial role in unraveling earthquake source processes. However, the multiscale nature of rupture propagation pose significant challenges, and classical numerical methods remain computationally expensive. To overcome this hurdle, we present a methodology that is both computationally efficient and quantitatively accurate. Specifically, we introduce a surrogate model, in the form of a Fourier Neural Operator, for emulating the nonlinear equations governing dynamic rupture propagation on frictional interfaces. This surrogate is trained on synthetic data generated by multiple physics-based dynamic rupture simulations and is then applied to unseen problems. The proposed methodology retains the accuracy of traditional multiscale methods at a significantly reduced computational cost, achieving a speedup of up to 400,000 compared to the state-of-the—art conventional methods. We evaluate this approach using various examples and demonstrate its efficacy in capturing the spacetime evolution of fault slip rates for a wide range of stress conditions. This development advances the state of the art of computational earthquake dynamics and opens new opportunities for accelerating physics-based rupture forecasts.