Chen, Y. orcid.org/0000-0003-3122-1829, de Ridder, S.A.L. orcid.org/0000-0002-0797-7442, Rost, S. orcid.org/0000-0003-0218-247X et al. (4 more authors) (2023) Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau. Journal of Geophysical Research: Solid Earth, 128 (12). ISSN 2169-9313
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
Keywords: | elliptical-anisotropy eikonal tomography; anisotropy; physics informed neural network; deep learning; surface waves; Tibet |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst of Geophysics and Tectonics (IGT) (Leeds) The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Institute for Applied Geosciences (IAG) (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Jan 2024 11:33 |
Last Modified: | 09 Jan 2024 11:33 |
Published Version: | http://dx.doi.org/10.1029/2023jb027378 |
Status: | Published |
Publisher: | American Geophysical Union (AGU) |
Identification Number: | 10.1029/2023jb027378 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207398 |