Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows
Abstract Full waveform inversion (FWI) is able to construct high‐resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Re...
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
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| Online Access: | https://doi.org/10.1029/2024JH000542 |
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| author | Feng Liu Haipeng Li Guangyuan Zou Junlun Li |
| author_facet | Feng Liu Haipeng Li Guangyuan Zou Junlun Li |
| author_sort | Feng Liu |
| collection | DOAJ |
| description | Abstract Full waveform inversion (FWI) is able to construct high‐resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open‐source AD‐based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD‐based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state‐of‐the‐art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large‐scale inversion associated with AD, the proposed framework adopts strategies such as mini‐batch and checkpointing. Through tests on synthetic and field data, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and development of new inversion strategies. |
| format | Article |
| id | doaj-art-2fa488a4803e4d6da048714d4fbbc6cd |
| institution | DOAJ |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-2fa488a4803e4d6da048714d4fbbc6cd2025-08-20T02:49:43ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000542Automatic Differentiation‐Based Full Waveform Inversion With Flexible WorkflowsFeng Liu0Haipeng Li1Guangyuan Zou2Junlun Li3School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai ChinaLaboratory of Seismology and Physics of Earth's Interior, School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaLaboratory of Seismology and Physics of Earth's Interior, School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaLaboratory of Seismology and Physics of Earth's Interior, School of Earth and Space Sciences University of Science and Technology of China Hefei ChinaAbstract Full waveform inversion (FWI) is able to construct high‐resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in simplifying solutions of various inverse problems, including FWI. In this study, we present an open‐source AD‐based FWI framework (ADFWI), which is designed to simplify the design, development, and evaluation of novel approaches in FWI with flexibility. The AD‐based framework not only includes forword modeling and associated gradient computations for wave equations in various types of media from isotropic acoustic to vertically or horizontally transverse isotropic elastic, but also incorporates a suite of objective functions, regularization techniques, and optimization algorithms. By leveraging state‐of‐the‐art AD, objective functions such as soft dynamic time warping and Wasserstein distance, which are difficult to apply in traditional FWI are also easily integrated into ADFWI. In addition, ADFWI is integrated with deep learning for implicit model reparameterization via neural networks, which not only introduces learned regularization but also allows rapid estimation of uncertainty through dropout. To manage high memory demands in large‐scale inversion associated with AD, the proposed framework adopts strategies such as mini‐batch and checkpointing. Through tests on synthetic and field data, we demonstrate the novelty, practicality and robustness of ADFWI, which can be used to address challenges in FWI and as a workbench for prompt experiments and development of new inversion strategies.https://doi.org/10.1029/2024JH000542automatic differentiationfull waveform inversionwave equationdeep prioruncertainty estimation with dropoutflexible workflow |
| spellingShingle | Feng Liu Haipeng Li Guangyuan Zou Junlun Li Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows Journal of Geophysical Research: Machine Learning and Computation automatic differentiation full waveform inversion wave equation deep prior uncertainty estimation with dropout flexible workflow |
| title | Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows |
| title_full | Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows |
| title_fullStr | Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows |
| title_full_unstemmed | Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows |
| title_short | Automatic Differentiation‐Based Full Waveform Inversion With Flexible Workflows |
| title_sort | automatic differentiation based full waveform inversion with flexible workflows |
| topic | automatic differentiation full waveform inversion wave equation deep prior uncertainty estimation with dropout flexible workflow |
| url | https://doi.org/10.1029/2024JH000542 |
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