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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2601.16068 |
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| _version_ | 1866909998076919808 |
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| author | Liang, Chaohua Peng, Xingliang Matsushima, Jun |
| author_facet | Liang, Chaohua Peng, Xingliang Matsushima, Jun |
| contents | 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 high computational cost in inverse problems. Although deep learning has shown promise in seismic applications, its performance is commonly limited by the need for large labeled datasets and weak enforcement of physical constraints.
In this study, we propose a unified physics-informed neural network (PINN) framework for forward modeling and parameter inversion of viscoacoustic wave propagation. By embedding the viscoacoustic wave equation into the learning process, the proposed framework accurately reproduces wavefields, attenuation, and phase characteristics, while enabling the simultaneous inversion of velocity and attenuation parameters from temporally sparse observations.
Numerical experiments demonstrate that the PINN approach achieves stable and reliable accuracy compared with finite-difference solutions, while exhibiting reduced sensitivity to spatial discretization. These results highlight the potential of PINNs as a data-efficient and physically consistent alternative for high-resolution seismic modeling and inversion in attenuative media. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16068 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Neural Networks for Viscoacoustic Wave Propagation: Forward Modelling, Inversion and Discretization Sensitivity Liang, Chaohua Peng, Xingliang Matsushima, Jun Geophysics 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 high computational cost in inverse problems. Although deep learning has shown promise in seismic applications, its performance is commonly limited by the need for large labeled datasets and weak enforcement of physical constraints. In this study, we propose a unified physics-informed neural network (PINN) framework for forward modeling and parameter inversion of viscoacoustic wave propagation. By embedding the viscoacoustic wave equation into the learning process, the proposed framework accurately reproduces wavefields, attenuation, and phase characteristics, while enabling the simultaneous inversion of velocity and attenuation parameters from temporally sparse observations. Numerical experiments demonstrate that the PINN approach achieves stable and reliable accuracy compared with finite-difference solutions, while exhibiting reduced sensitivity to spatial discretization. These results highlight the potential of PINNs as a data-efficient and physically consistent alternative for high-resolution seismic modeling and inversion in attenuative media. |
| title | Physics-Informed Neural Networks for Viscoacoustic Wave Propagation: Forward Modelling, Inversion and Discretization Sensitivity |
| topic | Geophysics |
| url | https://arxiv.org/abs/2601.16068 |