EFGW-UNet: A Deep-Learning-Based Approach for Weak Signal Recovery in Seismic Data

Recorded seismic data often are characterized by a low signal-to-noise ratio (SNR) that can hinder subsequent imaging and interpretation tasks. Thus, it is necessary to explore a method to recover weak signals from strong background noise. While numerous studies have demonstrated the effectiveness of deep-learning methods in seismic noise attenuation, enhancing their capability to recover weak signals under low SNR conditions remains an area for further exploration. To address this issue, we propose an edge-feature-guided wavelet U-Net (EFGW-UNet). In this novel architecture, we utilize the discrete wavelet transform (DWT) to replace the pooling operation deployed in the conventional U-Net, thereby maintaining more detailed information of effective signals. Meanwhile, we also design a dual decoder for edge detection to obtain the shape and edge information on the effective signals. Finally, to fuse multilevel image features and edge features, an attention feature fusion module is deployed. In the experimental part, we use synthetic and real data to illustrate the effectiveness of EFGW-UNet. Our results suggest better denoising performance than competitive methods, especially for weak signal recovery submerged in heavy noise.