BibTeX

@article{2504.15289v1,
Author        = {Yunlin Zeng and Huseyin Tuna Erdinc and Rafael Orozco and Felix Herrmann},
Title         = {Full-waveform variational inference with full common-image gathers and
  diffusion network},
Eprint        = {2504.15289v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {physics.geo-ph},
Abstract      = {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.},
Year          = {2025},
Month         = {Apr},
Url           = {http://arxiv.org/abs/2504.15289v1},
File          = {2504.15289v1.pdf}
}