Seismic signal denoising using Swin-Conv-UNet

Abstract

In recent years, a notable surge has occurred in the adoption of deep neural networks for addressing the challenges of seismic signal denoising. However, many existing methodologies are constrained by simplistic noise assumptions and exhibit limited generalisation capabilities, particularly when confronted with field seismic signals. Consequently, the need for a robust denoising solution tailored to the complexities of field seismic signals remains unresolved. To address this gap and effectively enhance the signal-to-noise ratio (SNR), we proposed a novel denoising approach. Central to our methodology is the incorporation of a custom-designed network structure that leverages the unique characteristics of seismic signals. Specifically, we introduced the swin-link-conv block, which amalgamates the modelling capabilities of the swin transformer block with the adaptive properties of the residual connection layer. This innovative building block was seamlessly integrated into the UNet architecture, which is a widely acclaimed framework for seismic signal processing. The resulting network architecture was a christened seismic swin-conv UNet (SSC-UNet). Extensive experiments conducted on both synthetic and field seismic signals underscore the efficacy of the proposed network structure. The SSC-UNet methodology achieved state-of-the-art denoising performance, demonstrating superior accuracy in removing additive white Gaussian noise (AWGN). This success not only validates the generalisation capabilities of SSC-UNet but also underscores its viability as a robust solution for field seismic signal denoising applications.