# An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing
## 3 Synthetic Seismic Data Processing Results  
To ascertain the efficacy and robustness of the proposed methodology, we have conducted an initial validation study using synthetic data. The synthetic seismic data employed in this study comprise 10,000 seismic traces with a sampling interval of 0.001 s. To facilitate the training and evaluation of the proposed methodology, the dataset was partitioned into a training set, a validation set, and a test set in an 8:1:1 ratio. Moreover, to augment the training data, a mirroring technique was utilized, thereby generating additional training samples and promoting greater model generalization. To further accelerate network convergence, the data were normalized separately before being fed into the network.  
To verify the denoising performance of deep well networks under different levels of noise, we have subjected the data to different levels of noise, resulting in a range of SNRs, and the SNRs evaluated were \(-11.363\) dB, \(-7.761\) dB, \(-3.829\) dB, \(4.695\) dB, \(8.221\) dB, and \(18.138\) dB, respectively.  
Initially, a series of ablation experiments are conducted, utilizing the \(-7.761\) dB dataset, to provide justification for the chosen network architecture (Table 1). Through a process of selective removal of specific components, the study aims to isolate and identify the critical elements that underpin the network's overall performance. Such a methodological approach enables a granular and detailed evaluation of the network's structural composition, allowing for a comprehensive and rigorous analysis of its functionality and capacity. As can be seen from Table 1, the use of ELU, CBAM, CA, or any two combinations on the Resnet network structure alone cannot fully improve the network denoising performance. Only when ELU, CBAM, and CA work together, the performance can be further improved.  
Then, in order to evince the efficacy of the suggested denoising methodology, we conducted a comparative analysis with two denoising methods: Resnet18 and wavelet threshold (WT). Table 2 and Figure 4 present the comparative effects of wavelet threshold, Resnet18, and ResATnet across various noise levels. It is evident that the wavelet threshold only exhibits a marginal advantage over ResATnet at lower noise levels. However, as the noise levels increase, ResATnet surpasses wavelet threshold in terms of ability. At higher noise levels, Resnet18 gradually manifests its efficacy, albeit its performance is still inferior to that of ResATnet.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Elu** & **CBAM** & **SAM** & **CAM** & **AvgPool** & **Output SNR(dB)** \\ \hline \(\surd\)* & & & & & 7.244 \\ \(\surd\) & & & & & \(\surd\) & 6.659 \\ \(\surd\) & \(\surd\) & & & & 4.128 \\ \(\surd\) & & \(\surd\) & & & 6.207 \\ \(\surd\) & & & \(\surd\) & & 7.379 \\  & \(\surd\) & & & \(\surd\) & 6.180 \\  & \(\surd\) & & \(\surd\) & & 7.154 \\  & \(\surd\) & \(\surd\) & & & 6.931 \\  & & \(\surd\) & & \(\surd\) & 6.628 \\  & & \(\surd\) & & \(\surd\) & 5.351 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 7.366 \\ \(\surd\) & \(\surd\) & & \(\surd\) & & 8.165 \\  & \(\surd\) & \(\surd\) & & \(\surd\) & 7.123 \\  & \(\surd\) & & \(\surd\) & \(\surd\) & 7.353 \\ \hline \hline \end{tabular}
* *  
Evidently, it becomes apparent that, at a SNR of \(-3.829\) dB, the outcomes yielded by the application of wavelet threshold denoising are essentially ineffectual in capturing the features of the original waveform. However, when the SNR is 18.138 dB, the wavelet threshold denoising method can achieve comparable denoising results with the neural network denoising method. This failure can be attributed to the inefficiency of the technique in effectively segregating the signal from the extraneous noise and its low-robustness for high levels of noise. Conversely, while the employment of the neural network approach also experiences a decline in denoising efficacy in the presence of heightened noise levels, it is still capable of approximating the genuine signal with a fair degree of fidelity.