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.