Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation

Abstract

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.

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Cited by in corpus (5)

• Deep Neural Helmholtz Operators for 3D Elastic Wave Propagation and Inversion
• Multiple-Input Fourier Neural Operator (MIFNO) for source-dependent 3D elastodynamics
• Reduced-order modeling for complex 3D seismic wave propagation
• Ambient Noise Full Waveform Inversion with Neural Operators
• Waveform-Based Probabilistic Seismic Hazard Analysis Using Ground-Motion Generative Models