Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers
| Title | Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers |
| Publication Type | Unpublished |
| Year of Publication | 2026 |
| Authors | Yunlin Zeng, Huseyin Tuna Erdinc, Felix J. Herrmann |
| Month | 3 |
| Keywords | Bayesian inference, CIG, deep learning, diffusion models, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE |
| Abstract | 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/cm³), with velocity quality confirmed by CIG focusing. |
| URL | https://slim.gatech.edu/Publications/Public/Submitted/2026/zeng2026IMAGEbjv/abstract.html |
| URL2 | |
| Citation Key | zeng2026IMAGEbjv |