Ambient Noise Full Waveform Inversion with Neural Operators

Caifeng Zou, Zachary E. Ross, Robert W. Clayton, Fan-Chi Lin, & Kamyar Azizzadenesheli

Submitted September 7, 2025, SCEC Contribution #14935, 2025 SCEC Annual Meeting Poster #TBD

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 dataset, which consists of several nodal transects collected across the San Gabriel, Chino, and San Bernardino basins in the Los Angeles metropolitan area.

Citation
Zou, C., Ross, Z. E., Clayton, R. W., Lin, F., & Azizzadenesheli, K. (2025, 09). Ambient Noise Full Waveform Inversion with Neural Operators. Poster Presentation at 2025 SCEC Annual Meeting.

Related Projects & Working Groups
Seismology