AI 中文总结
针对弹性参数从角度叠加地震数据中可靠估计的难题，提出基于引导扩散模型的反演工作流程，经无监督训练学习参数耦合，用扩散后验采样近似似然函数，在两个数据集上评估，相比基线能恢复更好剖面并实现不确定性量化。
AI 中文摘要
弹性参数是储层表征的基本岩石属性，但从角度叠加地震数据中可靠估计具有挑战性。传统确定性方法有局限，概率方法计算昂贵。本文提出用引导扩散模型进行弹性参数反演的工作流程，该模型在基准数据集和合成模型上无监督训练，学习弹性参数间非高斯统计耦合。采用扩散后验采样，基于Aki-Richards近似通过正向算子近似似然函数并注入数据一致性梯度校正。在两个数据集上评估框架，与两个基线比较，结果表明该框架能恢复更清晰岩性对比和更真实弹性剖面，还能通过多次反向扩散运行生成多个独立后验实现来实现不确定性量化。
英文摘要
Elastic parameters are fundamental rock properties for reservoir characterization, but their reliable estimation from angle-stack seismic data remains challenging due to strong nonlinearity and imperfect physical modeling. Conventional deterministic approaches based on linearized Zoeppritz approximations yield a single point estimate and cannot quantify solution uncertainty, while probabilistic methods are computationally expensive. To address these limitations, we present a workflow for elastic parameter inversion from angle-stack seismic data using a guided diffusion model as an implicit prior over the joint distribution of P-wave velocity, S-wave velocity, and density. The diffusion model is trained in an unsupervised manner on benchmark datasets and well-log-derived synthetic models, learning the non-Gaussian statistical coupling among the three elastic parameters. For guidance, we employ Diffusion Posterior Sampling (DPS), which approximates the likelihood function through a forward operator based on the Aki-Richards approximation and injects data-consistency gradient corrections at each reverse diffusion step, sampling from a posterior conditioned on the misfit between observed and modeled angle-stack data. We evaluate the framework on two datasets: the 2D Otway synthetic elastic model and field data from the Poseidon field, NW Shelf, Browse Basin, Australia, comparing it against two baselines: LSQR least-squares inversion and ADMM-based inversion with total variation regularization. Quantitative comparisons confirm that the diffusion-based framework recovers sharper lithological contrasts and geologically more realistic elastic profiles. Uncertainty quantification is achieved by generating multiple independent posterior realizations through repeated reverse diffusion runs, producing spatially resolved uncertainty maps for each elastic parameter.
