Physics-Informed Neural Networks for Viscoacoustic Wave Propagation: Forward Modelling, Inversion and Discretization Sensitivity

Seismic wave forward and inverse modeling are fundamental tools for subsurface imaging and geological hazard assessment. Conventional grid-based numerical methods, such as finite-difference and finite-element approaches, often require dense discretization and repeated forward simulations, leading to...

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Bibliographic Details

Published in:	arXiv (Cornell University)
Main Author:	Chaohua Liang
Other Authors:	Xingliang Peng, Jun Matsushima
Format:	Preprint
Published:	Cornell University 2026
Subjects:	Seismic Imaging and Inversion Techniques Model Reduction and Neural Networks Seismic Waves and Analysis physics-informed neural networks seismic forward modeling seismic inversion attenuation velocity inversion sparse seismic data
Online Access:	http://arxiv.org/abs/2601.16068 https://doi.org/10.48550/arxiv.2601.16068
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