June 11, 2024, 4:51 a.m. | Huseyin Tuna Erdinc, Rafael Orozco, Felix J. Herrmann

cs.CV updates on arXiv.org arxiv.org

arXiv:2406.05136v1 Announce Type: cross
Abstract: 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 …

abstract applications arxiv cs.ai cs.cv datasets diffusion diffusion models fidelity generative generative models modeling novel physics.geo-ph quality seismic study type

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