WISE: full-Waveform variational Inference via Subsurface Extensions
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929313041874944 |
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| author | Yin, Ziyi Orozco, Rafael Louboutin, Mathias Herrmann, Felix J. |
| author_facet | Yin, Ziyi Orozco, Rafael Louboutin, Mathias Herrmann, Felix J. |
| contents | We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_06230 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | WISE: full-Waveform variational Inference via Subsurface Extensions Yin, Ziyi Orozco, Rafael Louboutin, Mathias Herrmann, Felix J. Geophysics Artificial Intelligence Machine Learning Signal Processing Applications We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging. |
| title | WISE: full-Waveform variational Inference via Subsurface Extensions |
| topic | Geophysics Artificial Intelligence Machine Learning Signal Processing Applications |
| url | https://arxiv.org/abs/2401.06230 |