A Self-supervised Swin-Unet Method for Ground Roll Suppression Based on Fourier Positional Encoding and Masking Strategy

Summary To address the challenge in seismic exploration where strong-energy ground roll severely interferes with effective signals and conventional suppression methods tend to damage these signals, this paper proposes a self-supervised Swin-Unet network method for ground roll suppression based on Fourier Positional Encoding and a bespoke masking strategy. This method operates without the need for clean label data. By employing a specially designed fan-shaped masking strategy, it disrupts the spatio-temporal coherence of the ground roll, thereby guiding the network to learn the intrinsic characteristics of the effective signals and reconstruct the data. The core innovation lies in the introduction of Fourier Positional Encoding, which overcomes the inherent low-frequency bias of the Transformer architecture. This significantly enhances the network’s capability to model and recover high-frequency effective signals. Experimental results on synthetic data and field 2D/3D seismic datasets demonstrate that the proposed method not only effectively suppresses strong ground roll but also surpasses the traditional f – k filtering method in terms of signal fidelity, particularly in preserving deep, weak reflections and high-frequency components. This showcases its robustness and potential for application in complex seismic data processing.