CDDIP: Constrained Diffusion-Driven Deep Image Prior for Seismic Data Reconstruction

Paul Goyes-Peñafiel, Ulugbek S. Kamilov, Henry Arguello

Abstract: Seismic data frequently exhibit missing traces, substantially affecting subsequent seismic processing and interpretation. Deep learning-based approaches have demonstrated significant advancements in reconstructing irregularly missing seismic data through supervised and unsupervised methods. Nonetheless, substantial challenges remain, such as generalization capacity and computation time cost during the inference. This work introduces a reconstruction method that uses a pretrained generative diffusion model for image synthesis and incorporates deep image prior (DIP) to enforce data consistency when reconstructing missing traces in seismic data. The proposed method has demonstrated strong robustness and high reconstruction capability of poststack and prestack data with different levels of structural complexity, even in field and synthetic scenarios where test data were outside the training domain. This indicates that our method can handle the high geological variability of different exploration targets. Additionally, compared to other state-of-the-art seismic reconstruction methods using diffusion models, during inference, our approach reduces the number of sampling timesteps by up to $4\times $ . Our implementation is available at https://github.com/PAULGOYES/CDDIP.git.

External IDs:doi:10.1109/lgrs.2025.3545860