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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.05136 |
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| _version_ | 1866917688250466304 |
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| author | Erdinc, Huseyin Tuna Orozco, Rafael Herrmann, Felix J. |
| author_facet | Erdinc, Huseyin Tuna Orozco, Rafael Herrmann, Felix J. |
| contents | In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface applications. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requiring fully sampled training datasets. The results demonstrate that our generative model accurately captures long-range structures, aligns with ground-truth velocity models, achieves high Structural Similarity Index (SSIM) scores, and provides meaningful uncertainty estimations. This approach facilitates realistic subsurface velocity synthesis, offering valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05136 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models Erdinc, Huseyin Tuna Orozco, Rafael Herrmann, Felix J. Geophysics Artificial Intelligence Computer Vision and Pattern Recognition In this study, we introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Conventional methods rely on extensive, high-quality datasets, which are often inaccessible in subsurface applications. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requiring fully sampled training datasets. The results demonstrate that our generative model accurately captures long-range structures, aligns with ground-truth velocity models, achieves high Structural Similarity Index (SSIM) scores, and provides meaningful uncertainty estimations. This approach facilitates realistic subsurface velocity synthesis, offering valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling. |
| title | Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models |
| topic | Geophysics Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.05136 |