Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau

Abstract

We develop a novel approach for multi‐frequency, elliptical‐anisotropic eikonal tomography based on physics‐informed neural networks (pinnEAET). This approach simultaneously estimates the medium properties controlling anisotropic Rayleigh waves and reconstructs the traveltimes. The physics constraints built into pinnEAET's neural network enable high‐resolution results with limited inputs by inferring physically plausible models between data points. Even with a single source, pinnEAET can achieve stable convergence on key features where traditional methods lack resolution. We apply pinnEAET to ambient noise data from a dense seismic array (ChinArray‐Himalaya II) in the northeastern Tibetan Plateau with only 20 quasi‐randomly distributed stations as sources. Anisotropic phase velocity maps for Rayleigh waves in the period range from 10–40 s are obtained by training on observed traveltimes. Despite using only about 3% of the total stations as sources, our results show low uncertainties, good resolution and are consistent with results from conventional tomography.