Title: Constrained Probabilistic Diffusion Model for Seismic Data Reconstruction Using a Restoration Operator Based on a Deep Image Prior
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Seismic data reconstruction, deep learning, diffusion models, restoration operator, inverse problem

Abstract: Seismic data acquisition is of the utmost importance in the oil industry, as it allows the representation of subsurface geological features. Several factors affect the number of sources or receivers during the acquisition, impacting seismic data quality. Different methodologies have been developed for reconstruction based on generative models for seismic signals. Methods based on diffusion models (DM) have emerged in this context for seismic data reconstruction, guiding the image generation or the reverse process to solve the reconstruction problem using a closed-form solution and deep learning-based solvers. However, a disadvantage of these methodologies is that they cannot extract all the features necessary to represent the data domain, which is crucial for accurate reconstruction. As a result, the entire DM must be re-trained for each experiment, leading to high computational costs due to its complexity. We propose DM-RODIP, an alternative DM approach for seismic data reconstruction, where the reverse process of a pre-trained DM is guided toward a reconstruction problem solution using a restoration operator based on a Deep Image Prior. The proposed method was evaluated on synthetic and field data, demonstrating superior reconstruction performance with improvements of up to 10.2 dB in PSNR and 0.09 in SSIM for synthetic data, and 1.0 dB in PSNR and 0.04 in SSIM for field data, outperforming state-of-the-art methods.