Rapid Seismic Waveform Modeling and Inversion with Neural Operators

Abstract

Seismic waveform modeling is a powerful tool for determining earth structure models and unraveling earthquake rupture processes, but it is usually computationally expensive. We introduce a scheme to vastly accelerate these calculations with a recently developed machine learning paradigm called the neural operator. Once trained, these models can simulate a full wavefield at negligible cost. We use a U-shaped neural operator to learn a general solution operator to the 2D elastic wave equation from an ensemble of numerical simulations performed with random velocity models and source locations. We show that full waveform modeling with neural operators is nearly two orders of magnitude faster than conventional numerical methods, and more importantly, the trained model enables accurate simulation for velocity models, source locations, and mesh discretization distinctly different from the training dataset. The method also enables convenient full-waveform inversion with automatic differentiation.

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Cited by in corpus (6)

• Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation
• Instantaneous physics-based ground motion maps using reduced-order modeling
• 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
• Real-Time Reconstruction of Ground Motion During Small Magnitude Earthquakes: A Pilot Study