# An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing
## 4 Real Seismic Data Processing Results  
### Ground-Based Seismic Data  
This ground-based seismic data comprises 9356 seismic traces and a sampling interval of 0.002 s. The dataset is subjected to an unchanging process of partitioning into training, validation, and test sets with a proportional distribution of 8:1:1, and the training set was data-augmented, and all data were normalized before entering the network.  
In contrast to the synthetic seismic data, the ground-based seismic data present a greater degree of complexity, characterized by higher levels of waveform fluctuation. As shown in Table 3 and Figure 6, the neural network method outperforms wavelet thresholding in reducing noise in the data mentioned above. The ResATnet architecture shows the best performance in enhancing the quality of noisy data, while the Resnet18 architecture follows closely behind. Importantly, ResATnet exhibits significantly better performance in improving the quality of data with low signal-to-noise ratios compared to Resnet18. Although the denoising performance of ResATnet is better than that of Resnet18, as the level of noise increases, ResATnet has been observed to suffer from a faster rate of degradation in performance. As a result, the difference in performance between these two  
Figure 5: (**a–f**) illustrates the single-channel comparison of data denoised by three methods for different noise levels.  
approaches decreases gradually. At a SNR of \(-6.581\) dB, the difference in their outcomes is only \(0.791\) dB. The proposed method has limitations in reducing noise from the data. What is clear is that when the noise level is high, the neural network's performance remains compromised, and it cannot extract the intended signal accurately.  
It can be observed in Figure 7 that, as the level of noise increases, the effectiveness of the wavelet threshold approach in fitting the pristine signal progressively diminishes. Additionally, the Resnet18 method gradually loses its efficacy in isolating the noise components from the signal of interest. The diminishing capacity of this approach in attenuating the unwanted perturbations is a clear indication of its limited ability to cope with high levels of noise. In contrast to the Resnet18 approach, the ResATnet method exhibits a greater degree of resilience against the deleterious effects of noise interference. This observation highlights the capacity of the ResATnet method to mitigate the impact of noise perturbations on the denoising performance, albeit not entirely impervious to the effects of increased noise levels. As the noise level intensifies, the ResATnet method's denoising capability inevitably experiences a decrement, underscoring the inherent trade-off between noise reduction and signal preservation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input SNR(dB)** & **WT** & **Resnet18** & **ResATnet** \\ \hline
6.031 & 9.884 & 10.330 & 12.677 \\
4.401 & 8.690 & 9.599 & 11.766 \\
2.166 & 7.109 & 8.247 & 10.130 \\ \(-0.512\) & 5.448 & 7.399 & 8.697 \\ \(-3.051\) & 3.907 & 6.195 & 7.222 \\ \(-6.581\) & 2.080 & 4.037 & 4.828 \\ \hline \hline \end{tabular}
\end{table}
Table 3: WT, Resnet18, and ResATnet denoising effects under different noise levels under ground-based seismic data.  
Figure 6: Trend of the denoising results of WT, Resnet18, and ResATnet with varying SNRs of ground–based noisy seismic data.