Uncertainty propagation from crustal geologies to rock-site ground motion with a Fourier Neural Operator

Abstract

Seismic hazard analyses in the area of a nuclear installation must account for a large number of uncertainties, including limited geological knowledge. Combining the accuracy of physics-based simulations with the expressivity of deep neural networks can help to quantify the influence of crustal geological uncertainties on surface ground motion. This work uses a Factorised Fourier Neural Operator (F-FNO) to learn the relationship between 3D crustal heterogeneous geologies and time-dependent wavefields at the Earth’s surface up to a 5 Hz maximal frequency. The F-FNO is pretrained on a generic database and then, fine-tuned with only 250 samples targeted to the Le Teil region (South-Eastern France). The F-FNO correctly predicts the wave arrival times and the main wavefronts. As quantified by Goodness-Of-Fit (GOF) criteria, 83% of predictions have excellent phase GOF and 75% have very good envelope GOF. The Peak Ground Velocity and Pseudo-Spectral Acceleration are also accurate, especially for geologies with low-to-moderate heterogeneities. Thanks to the F-FNO speed, ground motion distributions can be easily computed and provide safety margins compared to 1D simulations. These results show that the F-FNO is an efficient surrogate model to quantify the range of ground motion a nuclear installation could face in the presence of geological uncertainties.