# An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing
###### Abstract
[16] have applied the DnResNeXt deep neural network architecture to effectively suppress noise in seismic data collected from challenging desert areas, achieving a superior denoising performance compared to the DnCNN method. Similarly, [PERSON] et al. [17] have contributed to the advancement of seismic data processing through the utilization of simultaneous denoising and interpolation via residual dense networks, leading to significant improvements in SNR for oceanic data. In addition, [PERSON] et al. [18] have presented a novel approach to seismic data denoising by integrating the residual structure with U-Net, culminating in the effective removal of desert seismic noise. These techniques demonstrate the potential of deep learning in seismic data processing and analysis, opening avenues for enhanced interpretation of subsurface geological structures. The efficacy of attentional mechanisms in facilitating the identification of salient information while simultaneously mitigating the impact of irrelevant inputs was initially demonstrated in domains, such as speech processing [19] and computer vision. Attention mechanisms can be divided into soft attention, hard attention, and self-attention. Among them, hard attention is not differentiable, and self-attention leads to performance problems due to its high computational complexity. Therefore, soft attention, which can be learned to obtain attention weights, has been more widely studied. For example, [PERSON] et al. [20] introduced a multiscale residual dense dual attention network for the purpose of SAR image denoising, and this approach effectively addresses speckle noise through a straightforward pixel attention module; [PERSON] et al. [21] proposed a novelty channel-spatial attention module to improve the efficacy of U-Net networks. Given the remarkable advancements achieved through the implementation of attention mechanisms in image processing, scholars have since incorporated this technique into the field of seismic data processing. By doing so, they have effectively executed innovative methodologies to augment the signal quality of seismic data. For example, [PERSON] et al. [22] demonstrated an innovative approach to noise suppression in seismic data by incorporating a multiscale and attention mechanism. This approach, which combines multiple scales with the capability of selective feature extraction, effectively mitigates noise, resulting in an improvement in the overall signal quality of the data; [PERSON] et al. [23] advocated for an integrative approach that merges attention mechanisms with neural networks to enhance the efficacy of tomographic detection; [PERSON] et al. [24] proposed a seismic phase pickup technique that integrates attention mechanisms with U-Net. The potential of this methodology to boost the exactitude and reliability of seismic phase identification is noteworthy. This study is driven by previous research to propose a novel methodology for cleaning seismic data that integrates Resnet and attention mechanism to optimize the performance of the Resnet, which is ResATnet. In particular, we added an attention module after the second convolutional layer of each residual block in Resnet, and we incorporated channel attention (CA) into the shortcut connections of the residual blocks. Additionally, we replaced the ReLU activation function used in the network with ELU. This approach has the potential to enhance the denoising effectiveness of the Resnet through the integration of attention mechanisms. Further details on these modifications can be found in the second part of the paper. The main contributions of this study consist of:  
* This proposed technique for denoising seismic data synergistically integrates a residual neural network and attention mechanism, exhibiting potential for improving the accuracy and dependability of seismic data analysis. * The validation of the proposed method, using synthetic and actual data, highlights its capacity as a reliable and robust technique for seismic data denoising.