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.

本研究介绍了一种使用扩散生成模型合成地下速度模型的新方法。该方法利用不完整的井和地震观测产生高保真度的速度样本，而无需完全采样的训练数据集。结果表明，我们的生成模型准确捕捉到了长距离结构，与地面真实速度模型一致，获得了高结构相似性指数（SSIM）分数，并提供了有意义的不确定性估计。这一方法促进了真实的地下速度合成，为全波形反演提供了有价值的输入，并增强了基于地震的地下模型。

基于不完整的井和成像地震观测数据的生成地统计建模与扩散模型