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arXiv · 2609.05476

Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers

Yunlin Zeng·Huseyin Tuna Erdinc·Felix J. Herrmann
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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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Yunlin Zeng, Huseyin Tuna Erdinc, Felix J. Herrmann. 2026-08-22. Bayesian Joint Velocity and Impedance Inversion via Diffusion Models Conditioned on Common Image Gathers. https://arxiv.org/abs/2609.05476

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