OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911072300040192 |
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| author | Someya, Masayoshi Yamada, Taisuke Okazaki, Tomohisa |
| author_facet | Someya, Masayoshi Yamada, Taisuke Okazaki, Tomohisa |
| contents | The Okada model is a widely used analytical solution for displacements and strains caused by a point or rectangular dislocation source in a 3D elastic half-space. We present OkadaTorch, a PyTorch implementation of the Okada model, where the entire code is differentiable; gradients with respect to input can be easily computed using automatic differentiation (AD). Our work consists of two components: a direct translation of the original Okada model into PyTorch, and a convenient wrapper interface for efficiently computing gradients and Hessians with respect to either observation station coordinates or fault parameters. This differentiable framework is well suited for fault parameter inversion, including gradient-based optimization, Bayesian inference, and integration with scientific machine learning (SciML) models. Our code is available here: https://github.com/msomeya1/OkadaTorch |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_17126 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters Someya, Masayoshi Yamada, Taisuke Okazaki, Tomohisa Geophysics Machine Learning The Okada model is a widely used analytical solution for displacements and strains caused by a point or rectangular dislocation source in a 3D elastic half-space. We present OkadaTorch, a PyTorch implementation of the Okada model, where the entire code is differentiable; gradients with respect to input can be easily computed using automatic differentiation (AD). Our work consists of two components: a direct translation of the original Okada model into PyTorch, and a convenient wrapper interface for efficiently computing gradients and Hessians with respect to either observation station coordinates or fault parameters. This differentiable framework is well suited for fault parameter inversion, including gradient-based optimization, Bayesian inference, and integration with scientific machine learning (SciML) models. Our code is available here: https://github.com/msomeya1/OkadaTorch |
| title | OkadaTorch: A Differentiable Programming of Okada Model to Calculate Displacements and Strains from Fault Parameters |
| topic | Geophysics Machine Learning |
| url | https://arxiv.org/abs/2507.17126 |