# A Strong Noise Reduction Network for Seismic Records
## 3 Results and Discussion
### Comparative Experiments and Analysis  
To demonstrate the denoising efficacy of our model, we conducted comparisons with the U-Net, DeepDenoiser, and DnRDB networks. The experimental outcomes were  
Figure 6: Denoising performance of mixed-frequency noise: (**I,III,V**) are the original signal, the noise signal, and the denoised signal in the time domain; and (**II,IV,VI**) are the time–frequency domain data of (**I,III,V**).  
Figure 7: Denoising performance of spike noise: (**I,III,V**) are the original signal, the noise signal, and the denoised signal in the time domain; and (**II,IV,VI**) are the time–frequency domain data of (**I,III,V**).  
assessed using the SNR, correlation coefficient, RMSE and arrival error. All the methods utilized data from an identical test set, with the SNR ranging from \(-14\) dB to \(0\) dB. The results, depicted in Figure 8, reveal that our model consistently achieves higher SNRs and correlation coefficients, alongside lower RMSEs and arrival errors across various noise levels, when contrasted with the U-Net, DeepDenoiser, and DnRDB models. This suggests a superior denoising performance with minimal waveform distortion by our model. Relative to the average SNR, as shown in Table 1, our model exhibits an improvement in the SNR by \(1.74\) dB, \(1.18\) dB, and \(3.66\) dB over the U-Net, DeepDenoiser, and DnRDB models, respectively. Additionally, the average correlation coefficients are enhanced by \(0.0050\), \(0.0044\), and \(0.0166\), while the RMSE values are reduced by \(0.0491\), \(0.0365\), and \(0.1263\), correspondingly. By adding the Inception module to the U-Net, the SNR and r of the denoised signal can be improved more and the RMSE can be reduced more, which proves that adding the Inception module proposed in this paper to the network can improve the ability of the network to distinguish between the signal and the noise and strengthen the denoising performance of the network.  
Figure 9 illustrates the waveforms after noise reduction using various models when the SNR of the added noise signal is \(-10\) dB. It can be seen that although U-Net, DnRDB, and DeepDenoiser have removed most of the noise, some of the noise has not been removed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Improvement of SNR** & **Improvement of r** & **Reduction of RMSE** \\ \hline U-Net & 16.94 & 0.3096 & 1.1881 \\ DeepDenoiser & 17.49 & 0.3102 & 1.2007 \\ DnRDB & 15.01 & 0.2980 & 1.1109 \\ U-Net + Inception & 18.39 & 0.3140 & 1.2267 \\ Our model & 18.67 & 0.3146 & 1.2372 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denoising results of the different networks.  
Figure 8: Comparison of the denoising effects of different networks: (**a**) improvement of the SNR; (**b**) correlation coefficient; (**c**) root mean square error; and (**d**) arrival time error.  
before the signal P-wave arrives at the time point, which means the P-wave first arrivals pickup of the denoised signals has errors. Our model denoises the signal best and can effectively remove the noise before the signal P-wave arrives, which means the P-wave first arrivals of the signal denoised by our model have errors. Our model has the best denoising effect, which can effectively remove the noise before the arrival of the P-wave of the signal, which makes the P-wave first arrivals of the denoised signal from our model more accurate, which is helpful for the subsequent seismic positioning.