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With the recent rise of neural operators, scientific machine learning offersnew solutions to quantify uncertainties associated with high-fidelity numericalsimulations. Traditional neural networks, such as Convolutional Neural Networks(CNN) or Physics-Informed Neural Networks (PINN), are restricted to theprediction of solutions in a predefined configuration. With neural operators,one can learn the general solution of Partial Differential Equations, such asthe elastic wave equation, with varying parameters. There have been very fewapplications of neural operators in seismology. All of them were limited totwo-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 groundmotion time series from a 3D geological description. We used a high-fidelitysimulation code, SEM3D, to build an extensive database of ground motionsgenerated by 30,000 different geologies. With this database, we show that theFNO can produce accurate ground motion even when the underlying geologyexhibits large heterogeneities. Intensity measures at moderate and largeperiods are especially well reproduced.  We present the first seismological application of Fourier Neural Operators in3D. Thanks to the generalizability of our database, we believe that our modelcan be used to assess the influence of geological features such as sedimentarybasins on ground motion, which is paramount to evaluating site effects.

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