发表机构
Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于扩散模型的多参数贝叶斯反演框架，利用两种互补的公共图像道集条件，联合恢复地下速度与声阻抗，并在Compass基准上验证了高精度。
AI 中文摘要
我们提出了一种多参数基于模拟的推理框架，用于从地震数据中联合贝叶斯恢复地下速度和声阻抗。一个基于分数的扩散模型以两种互补的公共图像道集（CIGs）为条件：一种编码反射率振幅的逆散射CIG和一种编码运动学速度误差的反ISIC CIG。该模型同时采样两个参数的后验分布。训练标签被有意解耦，以防止模型利用Gardner关系：速度目标被轻度平滑以匹配反ISIC CIG的长波长内容，而阻抗目标则保留未平滑的真实值。在Compass基准上，该模型实现了速度SSIM为0.967（RMSE 0.050 km/s）和阻抗SSIM为0.867（RMSE 0.279 km/s g/cm^3），速度质量通过CIG聚焦得到确认。
英文摘要
We present a multi-parameter simulation-based inference framework for joint Bayesian recovery of subsurface velocity and acoustic impedance from seismic data. A score-based diffusion model is conditioned on two complementary Common Image Gathers (CIGs): an inverse-scattering CIG encoding reflectivity amplitude and an anti-ISIC CIG encoding kinematic velocity errors. The model simultaneously samples the posterior distributions of both parameters. Training labels are deliberately decoupled to prevent the model from exploiting the Gardner relationship: velocity targets are lightly smoothed to match the long-wavelength content of the anti-ISIC CIG, while impedance targets retain the unsmoothed ground truth. On the Compass benchmark the model achieves velocity SSIM of 0.967 (RMSE 0.050 km/s) and impedance SSIM 0.867 (RMSE 0.279 km/s g/cm^3), with velocity quality confirmed by CIG focusing.
Comments8 pages, 11 figures. Accepted at IMAGE 2026
