تقرير
Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
| العنوان: | Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models |
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| المؤلفون: | Erdinc, Huseyin Tuna, Orozco, Rafael, Herrmann, Felix J. |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Physics (Other) |
| مصطلحات موضوعية: | Physics - Geophysics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2406.05136 |
| رقم الأكسشن: | edsarx.2406.05136 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |