Work

Seismic random noise attenuation with diffusion transformer

Year: 2026

Type: article

Abstract: Seismic data are often contaminated by random noise, which obscures weak reflections and complicates seismic interpretation. In this work, we propose a Vision Transformer-based diffusion model for seismic random noise attenuation. The denoising task is formulated within a diffusion framework, transforming the ill-posed problem into a sequence of stable and progressive denoising steps. This generative approach effectively alleviates the mean-collapse and over-smoothing issues inherent in traditional CNN and Transformer-based regression methods, thereby facilitating the recovery of high-frequency seismic details. We adopt a Transformer-based architecture with strong global modeling capability as the backbone of the diffusion model, enabling a global receptive field through self-attention mechanisms. Comparative experiments conducted on synthetic and field 2D seismic datasets confirm the clear performance advantage of the proposed method over f–k filtering, UNet, and standard diffusion models, achieving superior signal preservation with substantially reduced noise residue and signal leakage.

Source: Journal of Geophysics and Engineering

DOI: 10.1093/jge/gxag042

Authors: Cheng Zhao, Zhenbo Guo, Fei Li, Tianxiang Gao

Institutions: China National Petroleum Corporation (China), Harbin Institute of Technology

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,550

Funders: National Science and Technology Major Project, National Key Research and Development Program of China