SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation
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| Format: | Preprint |
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2025
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| _version_ | 1866916683324588032 |
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| author | Mu, Xinru Cheng, Shijun Alkhalifah, Tariq |
| author_facet | Mu, Xinru Cheng, Shijun Alkhalifah, Tariq |
| contents | Accurate separation of P- and S-waves is essential for multi-component seismic data processing, as it helps eliminate interference between wave modes during imaging or inversion, which leads to high-accuracy results. Traditional methods for separating P- and S-waves rely on the Christoffel equation to compute the polarization direction of the waves in the wavenumber domain, which is computationally expensive. Although machine learning has been employed to improve the computational efficiency of the separation process, most methods still require supervised learning with labeled data, which is often unavailable for field data. To address this limitation, we propose a wavefield separation technique based on the physics-informed neural network (PINN). This unsupervised machine learning approach is applicable to unlabeled data. Furthermore, the trained PINN model provides a mesh-free numerical solution that effectively captures wavefield features at multiple scales. Numerical tests demonstrate that the proposed PINN-based separation method can accurately separate P- and S-waves in both homogeneous and heterogeneous media. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_07544 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation Mu, Xinru Cheng, Shijun Alkhalifah, Tariq Geophysics Accurate separation of P- and S-waves is essential for multi-component seismic data processing, as it helps eliminate interference between wave modes during imaging or inversion, which leads to high-accuracy results. Traditional methods for separating P- and S-waves rely on the Christoffel equation to compute the polarization direction of the waves in the wavenumber domain, which is computationally expensive. Although machine learning has been employed to improve the computational efficiency of the separation process, most methods still require supervised learning with labeled data, which is often unavailable for field data. To address this limitation, we propose a wavefield separation technique based on the physics-informed neural network (PINN). This unsupervised machine learning approach is applicable to unlabeled data. Furthermore, the trained PINN model provides a mesh-free numerical solution that effectively captures wavefield features at multiple scales. Numerical tests demonstrate that the proposed PINN-based separation method can accurately separate P- and S-waves in both homogeneous and heterogeneous media. |
| title | SeparationPINN: Physics-Informed Neural Networks for Seismic P- and S-Wave Mode Separation |
| topic | Geophysics |
| url | https://arxiv.org/abs/2504.07544 |