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Abstract Numerical simulations of seismic wave propagation are crucial for investigating velocity structures and improving seismic hazard assessment. However, standard methods such as finite difference or finite element are computationally expensive. Recent studies have shown that a new class of machine learning models, called neural operators, can solve the elastodynamic wave equation orders of magnitude faster than conventional methods. Full waveform inversion is a prime beneficiary of the accelerated simulations. Neural operators, as end‐to‐end differentiable operators, combined with automatic differentiation, provide an alternative approach to the adjoint‐state method. State‐of‐the‐art optimization techniques built into PyTorch provide neural operators with greater flexibility to improve the optimization dynamics of full waveform inversion, thereby mitigating cycle‐skipping problems. In this study, we demonstrate the first application of neural operators for full waveform inversion on a real seismic data set, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area. 

Abstract LAB2022 is a new temporary array consisting of 273 geophones that was deployed in the Los Angeles basin for one month during the summer of 2022. The array was designed to improve the 3D seismic velocity model of the basin through passive seismic imaging, which is crucial for both earthquake hazard assessment and the understanding of the region’s tectonic evolution. The sensors are 3C 5 Hz Zland and Smart Solo instruments. The data has been archived at the EarthScope SAGE Data Management Center and will be publicly available in summer 2025.