# An Attention-Based Residual Neural Network for Efficient Noise Suppression in Signal Processing
## 5 Conclusions and Discussion  
In this paper, we present a novel approach to enhancing the performance of residual neural networks through the integration of an attention mechanism. By incorporating an attention mechanism into the residual neural network, we are able to selectively focus on the informative features of the data and effectively denoise both synthetic and actual datasets. To evaluate the effectiveness of our proposed method, we conducted extensive experiments on various datasets. From the experiments, we can find that, for synthetic seismic data with little waveform fluctuation, in contrast to traditional denoising methods, which are often limited in their ability to effectively filter out high levels of noise, our neural network denoising method excels in these challenging conditions. By leveraging the power of deep learning techniques, our method is able to capture the complex relationships between noisy and clean seismic data and effectively remove unwanted noise, even in the most difficult scenarios. As the seismic waveforms become increasingly complex, our proposed ResATnet method consistently outperformed traditional denoising methods, but Resnet can hardly learn effective information on the complex STanford EArthquake Dataset. This demonstrates the stability of the ResATnet method of denoising. Although wavelet thresholding methods also have stability, the conventional wavelet thresholding method requires a significant amount of time and effort to achieve optimal results. This is due in part to the need to manually select a suitable wavelet basis and perform multiple layers of decomposition. In our experiments on real data denoising, wavelet thresholds would need to be decomposed in seven or more layers under a suitable wavelet basis to obtain better denoising results, and this is a task that can be both tedious and time-consuming. The complexity of the waveform structure can impact the denoising performance of the neural network due to the complexity of the subsurface structure. Through actual data experiments, we observed that Resnet, which effectively denoised ground-based seismic data, was unable to effectively denoise the STanford EArthquake Dataset. Similarly, the denoising performance of ResATnet is degraded when processing the STanford EArthquake Dataset under similar input SNR conditions. Furthermore, ResATnet's ability to distinguish between signal and noise is limited when their amplitudes are similar, as shown in Figure 8e before the 10 th second and after the 15 th second. The denoising performance of neural networks can be affected by the complexity of the waveform structure, which is in turn influenced by the complexity of the subsurface structure. These, together with noise, cause ResATnet's denoising performance to degrade faster than Resnet18 as the noise level increases. The original goal of this study was to identify a denoising method that would perform well across a wide range of seismic data. ResATnet meets this expectation, but there are still limitations to its performance. Additionally, ResATnet has two limitations that should be noted. The first issue with ResATnet is its training efficiency, as each cycle of ResATnet takes 5.043 s longer on average than that of Resnet. The increased number of network parameters leads to network inefficiency, which is a drawback that needs to be  
Figure 8: (**a**) is noisy data, (**b**) is the noise in (**a**), which comes from the STanford EArthquake Dataset, and (**c**–**e**) illustrates the single-channel comparison of three methods. improved in next versions. The second point is the ability of neural networks to extract effective features from the data, which are significantly hampered in the presence of high levels of noise. Addressing this challenge and devising effective methods for enhancing the network's ability to extract key features from noisy data are critical areas of future research. T.L. : conceptualization, methodology, software; L.H.