Accurately characterizing migration velocity models is crucial for a wide range of geophysical applications, from hydrocarbon exploration to monitoring of CO2 sequestration projects. Traditional velocity model building methods such as Full-Waveform Inversion (FWI) are powerful but often struggle with the inherent complexities of the inverse problem, including noise, limited bandwidth, receiver aperture and computational constraints. To address these challenges, we propose a scalable methodology that integrates generative modeling, in the form of Diffusion networks, with physics-informed summary statistics, making it suitable for complicated imaging problems including field datasets. By defining these summary statistics in terms of subsurface-offset image volumes for poor initial velocity models, our approach allows for computationally efficient generation of Bayesian posterior samples for migration velocity models that offer a useful assessment of uncertainty. To validate our approach, we introduce a battery of tests that measure the quality of the inferred velocity models, as well as the quality of the inferred uncertainties. With modern synthetic datasets, we reconfirm gains from using subsurface-image gathers as the conditioning observable. For complex velocity model building involving salt, we propose a new iterative workflow that refines amortized posterior approximations with salt flooding and demonstrate how the uncertainty in the velocity model can be propagated to the final product reverse time migrated images. Finally, we present a proof of concept on field datasets to show that our method can scale to industry-sized problems.

翻译：准确刻画偏移速度模型对于从油气勘探到二氧化碳封存项目监测等一系列地球物理应用至关重要。传统速度建模方法（如全波形反演）功能强大，但常因反演问题固有的复杂性而受限，包括噪声、有限带宽、接收孔径及计算约束。为应对这些挑战，我们提出一种可扩展的方法论，将扩散网络形式的生成建模与物理信息摘要统计量相结合，使其适用于包含野外数据集的复杂成像问题。通过针对劣质初始速度模型定义基于地下偏移距成像体的摘要统计量，我们的方法能够以计算高效的方式生成偏移速度模型的贝叶斯后验样本，从而提供实用的不确定性评估。为验证该方法，我们引入一系列测试来评估推断速度模型的质量及推断不确定性的质量。利用现代合成数据集，我们再次确认了使用地下成像道集作为条件观测量的优势。针对包含盐体的复杂速度建模，我们提出一种新的迭代工作流程，通过盐体填充技术改进摊销后验近似，并展示了速度模型中的不确定性如何传递至最终产品——逆时偏移图像。最后，我们在野外数据集上进行了概念验证，表明该方法可扩展至工业级规模问题。