Wavelet threshold denoising [6; 7; 8; 9; 10; 11; 12] transforms signals from the time domain to the time-frequency domain through wavelet transform, which is suitable for processing non-stationary signals and has a reasonable resolution in both time and frequency domains. However, this method needs to select the number of wavelet decomposition layers and also select appropriate thresholds to quantify the wavelet coefficients of each layer after decomposition, resulting in an algorithm that relies on threshold selection and limited noise reduction. EMD is a self-adaptive signal analysis method proposed by [PERSON] et al. [14] in 1998. This method can decompose any type of signal into a finite number of intrinsic mode functions (IMFs) in theory, which is suitable for the analysis of non-linear and non-stationary signal sequences. In the process of denoising, a seismic signal with noise is decomposed into a limited number of IMFs and a clean seismic signal is obtained by removing the noise's IMFs. EMD is mainly based on the timescale characteristics of the signal itself and does not need to set the basic function or threshold in advance, but it also has some problems, such as mode aliasing and difficulty in selecting an IMF. To sum up, traditional seismic-signal-denoising methods require more manual participation and also need to select different threshold functions or basis functions for different types of noise, which leads to problems, such as difficulty in guaranteeing the accuracy of denoising and high computational cost. In recent years, deep learning technology has continued to evolve, and it has achieved excellent results in image classification, image noise reduction, speech noise reduction, and other fields [15; 16; 17; 18; 19; 20; 21; 22] with its self-adaptive feature learning and strong classification ability. Therefore, geophysicists began to use deep learning technology to self-adaptively reduce the noise in the co-frequency band in seismic signals. In 2018, [PERSON] et al. [23] used a convolutional neural network (CNN) to denoise seismic signals. However, the co-band noise in a complex environment has the characteristics of no fixed waveform, non-stationary feature, high energy, and the same frequency band range as effective signals, which leads to poor denoising ability of the common CNN for co-band noise, serious signal waveform distortion after denoising, and a low SNR. In 2018, [PERSON] et al. [24] adopted the residual learning strategy; this method used noise as the label during model training to make the model self-adaptively learn the noise in the signals and finally achieved the purpose of denoising by removing the noise recognized by the model. Although this method mitigates the problems of gradient vanishing and gradient exploding caused by deepening the network, the features of noise are more complex than seismic signals and the denoising ability of the model is limited, resulting in serious waveform distortion and a low SNR after denoising. In 2019, [PERSON] et al. [25] used the symmetric encoder-decoder structure of the U-Net model to denoise seismic signals. The model has a strong ability of feature extraction, which can extract seismic signal feature maps of different scales and fuse the signal feature maps extracted by the encoder with the decoder through feature concatenating [26]. However, the down-sampling process of the model encoder is realized by reducing the feature map of seismic signals, which inevitably has the problem of losing the details of the seismic signals. In 2021, [PERSON] et al. [27] designed the denoising residual dense block (DnRDB) model. This model deepens the depth of the network using dense skip connections to enhance the multiplexing of signal features and further mitigate the problem of signal detail loss during the denoising process of seismic signals.