# An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing
###### Abstract  
The incorporation of effective denoising techniques is a crucial requirement for seismic data processing during the acquisition phase due to the inherent susceptibility of the seismic data acquisition process to various forms of interference, such as random and coherent noise. For random noise, the Residual Neural Network (Resnet), with its notable ability to effectively suppress noise in seismic data, has garnered widespread utilization in removing unwanted disturbances or interference due to its elegant simplicity and outstanding performance. Despite the considerable advancements achieved by conventional Resnet in the field of suppressing noise, it is irrefutable that there is still room for amelioration in their ability to filter out unwanted disturbances. As a result, this paper puts forth a novel attention-based methodology for Resnet, intended to overcome the present constraints and attain an optimal seismic signal enhancement. Specifically, we add the convolutional block attention module (CBAM) after the convolutional layer of the residual module and add channel attention on the shortcut connections to filter out the disturbance. We replace the commonly used ReLU activation function in the network with ELU, which is better suited for suppressing seismic noise. Empirical assessments conducted on both synthetic and authentic datasets have demonstrated the efficacy of the proposed methodology in amplifying the denoising prowess of Resnet. Our proposed method remains stable even when dealing with seismic data that has complex waveforms. The findings of this investigation evince that the recommended approach furnishes a substantial augmentation in the signal-to-noise ratio (SNR), thereby facilitating the efficient and robust extraction of the underlying signal from the noisy observations. attention mechanism; noise suppression; residual neural network; random noise; wavelet coefficient threshold; ELU +
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resultant coefficients. Finally, the processed coefficients are reverse transformed to the original domain to yield a denoised seismic signal. For instance, a variety of techniques may be employed, including, but not limited to, the Fourier transform, radon transform [4], wavelet transform, curvelet transform, contourlet transform [5], shearlet transform [6], seislet transform [7], KSVD, and DDTF dictionary [8]. The methods in question can effectively extract salient signal features while simultaneously segregating them from noise. Nonetheless, their utilization necessitates a multitude of iterative experiments for the purpose of parameter selection, thus leading to superior outcomes. Decomposition-based methods, such as EMD [9], SVD [10], and SSA [11], strive to decompose the seismic signal into its constituent principal components. The purpose of this endeavor is to eliminate residual components that are intrinsically associated with extraneous perturbations, thereby restoring the original signal. However, to achieve this goal, it is necessary to determine the optimal number of decomposition layers, such that residual components are solely constituted of undesirable perturbations. The utility of deep learning as an efficacious feature extraction tool in seismic data processing has been extensively implemented. As an illustration of the diverse applications of deep learning in seismic data processing, [PERSON] et al. [12] utilized generative adversarial networks (GAN) for seismic data interpolation; [PERSON] et al. [13] proposed a deep semi-supervised neural network for automatic seismic first break picking; [PERSON] et al. [14] introduced long short-term memory (LSTM) networks to enhance the accuracy of inversion; [PERSON] et al. [15] presented the use of point-spread function-based convolution to generate synthetic samples for fault detection. With regards to the field of seismic data denoising, [PERSON] et al.