Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
<|reference_start|>Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models: 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.<|reference_end|>
@article{erdinc2024generative,
    title={Generative Geostatistical Modeling from Incomplete Well and Imaged
  Seismic Observations with Diffusion Models},
    author={Huseyin Tuna Erdinc, Rafael Orozco, and Felix J. Herrmann},
    journal={arXiv preprint arXiv:2406.05136},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.05136},
    primaryClass={physics.geo-ph cs.AI cs.CV}
}