Chen, Y, de Ridder, SAL orcid.org/0000-0002-0797-7442, Rost, S orcid.org/0000-0003-0218-247X
et al. (3 more authors)
(2022)
Eikonal Tomography With Physics-Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau.
Geophysical Research Letters, 49 (21).
e2022GL099053.
ISSN 0094-8276
Abstract
We present a novel eikonal tomography approach using physics-informed neural networks (PINNs) for Rayleigh wave phase velocities based on the eikonal equation. The PINN eikonal tomography (pinnET) neural network utilizes deep neural networks as universal function approximators and extracts traveltimes and velocities of the medium during the optimization process. Whereas classical eikonal tomography uses a generic non-physics based interpolation and regularization step to reconstruct traveltime surfaces, optimizing the network parameters in pinnET means solving a physics constrained traveltime surface reconstruction inversion tackling measurement noise and satisfying physics. We demonstrate this approach by applying it to 25 s surface wave data from ChinArray II sampling the northeastern Tibetan plateau. We validate our results by comparing them to results from conventional eikonal tomography in the same area and find good agreement.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022. 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. |
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: | 02 Dec 2022 16:35 |
Last Modified: | 02 Dec 2022 16:35 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1029/2022gl099053 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:193692 |