Self-Supervised Diffusion Model for 3-D Seismic Data Reconstruction

TL;DR

This paper introduces a self-supervised diffusion model tailored for 3D seismic data reconstruction, overcoming limitations of prior methods by handling complex data distributions without requiring large paired datasets.

Contribution

The paper presents a novel self-supervised diffusion model with a variational time-spatial module specifically designed for 3D seismic data reconstruction, addressing data complexity and data scarcity issues.

Findings

Outperforms existing methods in synthetic and field experiments.

Effectively handles complex and time-varying seismic data distributions.

Requires no large paired datasets for training.

Abstract

Seismic data reconstruction is an effective tool for compensating nonuniform and incomplete seismic geometry. Compared with methods for 2D seismic data, 3D reconstruction methods could consider more spatial structure correlation in seismic data. In the early studies, 3D reconstruction methods are mainly theory-driven and have some limitations due to their prior assumptions on the seismic data. To release these limitations, deep learning-based reconstruction methods rise and show potential in dealing with reconstruction problems. However, there are mainly two shortcomings in existing deep learning-methods. On the one hand, most of existing deep learning-based methods adopt the convolutional neural network, having some difficulties in dealing with data with complex or time-varying distributions. Recently, the diffusion model has been reported to possess the capability to solve data with complex distributions by gradually complicating the distribution of data to optimize the network. On the other hand, existing methods need enough paired-data to train the network, which are very hard to obtain especially for the starved 3D seismic data. Deep prior-based unsupervised and sampling-based self-supervised networks offer an available solution to this problem. In this paper, we develop a self-supervised diffusion model (S2DM) for 3D seismic data reconstruction. The proposed model mainly contains a diffusion restoration model and a variational time-spatial module. Extensive synthetic and field experiments demonstrate the superiority of the proposed S2DM algorithm.