Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers
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/cm^3), with velocity quality confirmed by CIG focusing.
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BibTeX
@misc{zeng2026bayesian,
title = {{Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers}},
author = {Yunlin Zeng and Huseyin Tuna Erdinc and Felix J. Herrmann},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.05476},
eprint = {2609.05476},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.05476},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers
AU - Yunlin Zeng
AU - Huseyin Tuna Erdinc
AU - Felix J. Herrmann
PY - 2026
DA - 2026/09/01
PB - arXiv
DO - 10.48550/arxiv.2609.05476
UR - https://arxiv.org/abs/2609.05476
AB - 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.
ER -