Related papers: Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models

URL: http://arxiv.org/abs/2406.05136v1
Date: Thu, 16 May 2024 20:30:43 GMT
Title: Generative Geostatistical Modeling from Incomplete Well and Imaged Seismic Observations with Diffusion Models
Authors: Huseyin Tuna Erdinc, Rafael Orozco, Felix J. Herrmann,
Abstract summary: We introduce a novel approach to synthesizing subsurface velocity models using diffusion generative models. Our method leverages incomplete well and seismic observations to produce high-fidelity velocity samples without requiring fully sampled training datasets.
Score: 0.24578723416255752
License: http://creativecommons.org/licenses/by/4.0/
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 meaningful uncertainty estimations. This approach facilitates realistic subsurface velocity synthesis, offering valuable inputs for full-waveform inversion and enhancing seismic-based subsurface modeling.