Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau

Yunpeng Chen; Sjoerd A. L. Ridder; Sebastian RostZhen GuoXiaoyang WuYongshun Chen

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Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau

机译：Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau

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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.

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《Geophysical Research Letters》 |2022年第21期|n/a-n/a|共9页
作者
Yunpeng Chen; Sjoerd A. L. Ridder; Sebastian RostZhen GuoXiaoyang WuYongshun Chen;
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作者单位

University of Leeds;

Southern University of Science and Technology;

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正文语种英语
中图分类地球物理学;
关键词
eikonal tomography; physics informed neural network; machine learning; surface waves; Tibet; ChinArray;

Eikonal Tomography With Physics‐Informed Neural Networks: Rayleigh Wave Phase Velocity in the Northeastern Margin of the Tibetan Plateau

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