Unsupervised Diffusion Model for Seismic Deconvolution

Abstract

Seismic data deconvolution is vital for enhancing resolution and accurate subsurface interpretation. Traditional methods heavily rely on predefined assumptions that limit their robustness to noisy data. As state-of-the-art generative models, diffusion models excel in capturing accurate prior distributions, which are beneficial to inversion. Moreover, diffusion models inherently resist noise due to their training in reverse noisy processes. Building on this foundation, we introduce an unsupervised diffusion model for seismic deconvolution, leveraging diffusion posterior sampling (DPS) to incorporate observed seismic data into the sampling process to guide high-accuracy reflectivity generation. Unlike traditional single-trace approaches, our method performs deconvolution across entire 2-D profiles, effectively capturing spatial continuity. Though solely trained on synthetic data, our method exhibits satisfactory performance when applied to synthetic and field datasets, demonstrating strong noise resistance and remarkable generalization capabilities.