Seismic Wave Propagation and Inversion with Neural Operators

Angela F. Gao, Yan Yang, Jorge A. Castellanos, Zachary E. Ross, Kamyar Azizzadenesheli, & Robert W. Clayton

Published August 12, 2021, SCEC Contribution #11309, 2021 SCEC Annual Meeting Poster #196 (PDF)

Seismic wave propagation forms the basis for most aspects of seismological research, yet solving the wave equation is a major computational burden that inhibits the progress of research. This is exaspirated by the fact that new simulations must be performed when the velocity structure or source location is perturbed. Here, we explore a prototype framework for learning general solutions using a recently developed machine learning paradigm called Neural Operator. A trained Neural Operator can compute a solution in negligible time for any velocity structure or source location. We develop a scheme to train Neural Operators on an ensemble of simulations performed with random velocity models and source locations. As Neural Operators are grid-free, it is possible to evaluate solutions on higher resolution velocity models than trained on, providing additional computational efficiency. We illustrate the method with the 2D acoustic wave equation and demonstrate the method's applicability to seismic tomography, using reverse mode automatic differentiation to compute gradients of the wavefield with respect to the velocity structure. The developed procedure is nearly an order of magnitude faster than using conventional numerical methods for full waveform inversion.

Key Words
wave propagation, forward model, inverse tomography

Citation
Gao, A. F., Yang, Y., Castellanos, J. A., Ross, Z. E., Azizzadenesheli, K., & Clayton, R. W. (2021, 08). Seismic Wave Propagation and Inversion with Neural Operators. Poster Presentation at 2021 SCEC Annual Meeting.

Related Projects & Working Groups
Seismology