Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
This work addresses the need for efficient seismic hazard assessment by providing a novel 3D surrogate model for seismologists, though it is incremental as it extends existing neural operator methods to a new domain.
The authors tackled the problem of predicting 3D seismic wave propagation by applying a Fourier Neural Operator (FNO) to learn solutions from a database of 30,000 geological simulations, achieving accurate ground motion predictions even with large heterogeneities, especially at moderate and large periods.
With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configuration. With neural operators, one can learn the general solution of Partial Differential Equations, such as the elastic wave equation, with varying parameters. There have been very few applications of neural operators in seismology. All of them were limited to two-dimensional settings, although the importance of three-dimensional (3D) effects is well known. In this work, we apply the Fourier Neural Operator (FNO) to predict ground motion time series from a 3D geological description. We used a high-fidelity simulation code, SEM3D, to build an extensive database of ground motions generated by 30,000 different geologies. With this database, we show that the FNO can produce accurate ground motion even when the underlying geology exhibits large heterogeneities. Intensity measures at moderate and large periods are especially well reproduced. We present the first seismological application of Fourier Neural Operators in 3D. Thanks to the generalizability of our database, we believe that our model can be used to assess the influence of geological features such as sedimentary basins on ground motion, which is paramount to evaluating site effects.