Accurate seismic imaging and velocity estimation are essential for subsurface characterization. Conventional inversion techniques, such as full-waveform inversion, remain computationally expensive and sensitive to initial velocity models. To address these challenges, we propose a simulation-based inference framework with conditional elucidated diffusion models for posterior velocity-model sampling. Our approach incorporates both horizontal and vertical subsurface offset common-image gathers to capture a broader range of reflector geometries, including gently dipping structures and steep dipping layers. Additionally, we introduce the background-velocity model as an input condition to enhance generalization across varying geological settings. We evaluate our method on the SEAM dataset, which features complex salt geometries, using a patch-based training approach. Experimental results demonstrate that adding the background-velocity model as an additional conditioning variable significantly enhances performance, improving SSIM from 0. 717 to 0. 733 and reducing RMSE from 0. 381\, km/s to 0. 274\, km/s. Furthermore, uncertainty quantification analysis shows that our proposed approach yields better-calibrated uncertainty estimates, reducing uncertainty calibration error from 6. 68\, km/s to 3. 91\, km/s. These results show robust amortized seismic inversion with uncertainty quantification.
Zeng et al. (Sun,) studied this question.
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