## 4 Experiments and Analysis
### Comparative Experiments and Analysis  
To further verify the effectiveness of the ARDU model, it was compared with wavelet threshold denoising, the DnRDB model, and the U-Net model and the experimental results were quantified using the SNR, \(r\), and RMSE. All methods adopted the same experimental data; the selected experimental data were the seismic signals with a high SNR, medium-high-frequency noise, low-frequency noise, and mixed-frequency band noise; the length of the selected signals contained 3000 sampling points; and the seismic signals and the three kinds of noise were synthesized into the noise-containing seismic signals with SNRs of \(-6\) dB, \(0\) dB, and \(6\) dB, respectively. The denoising results using different algorithms are shown in Tables 1-3 and in Figures 12-14. Among them, the figures show the denoising results of different denoising methods for seismic signals containing different levels of mixed-band noise; (a) and (b) are the time domain and time-frequency domain seismic signals with labels; (c) and (d) are the time domain and time-frequency domain seismic signals with noise; and (e) and (f), (g) and (h), (i) and (j), and (k) and (l) are denoising signals in the time domain and time-frequency domain of the wavelet threshold, DnRDB model, U-Net model, and ARDU model, respectively. It can be seen from Table 1 and Figure 12 that when dealing with noise-containing seismic signals with an SNR of \(-6\) dB, the SNR, \(r\), and RMSE of the ARDU model improved the most and the effect was the best among several denoising methods, followed by the U-Net model. The SNR and \(r\) of the ARDU model were 3.81 dB and 16.29% higher than those of the U-Net model, respectively, which indicates that the ARDU model can effectively remove co-band noise and improve the quality of seismic signals. The RMSE reduced by 33.02%, and it was the lowest, which shows that the degree of signal waveform distortion after denoising with the ARDU model is the smallest. It can be seen from Table 2 and Figure 13 that when dealing with seismic signals with an SNR of 0 dB, the ARDU model had the greatest improvement in the SNR, \(r\), and RMSE of the seismic signals with noise and the denoising effect was the best. Compared to the U-Net model, the SNR and \(r\) of the ARDU model improved by 2.83 dB and 7.24%, respectively, and the RMSE reduced by 28.85%, which indicates that the quality of the signals significantly improves after denoising with the ARDU model and the signal distortion is the smallest. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Denoising Method**} & \multicolumn{3}{c}{**Seismic Signals with an SNR of 6 dB**} \\ \cline{2-4}  & **Signal-to-Noise** & **Correlation** & **Root-Mean-Square** \\  & **Ratio (SNR)** & **Coefficient (\(r\))** & **Error (RMSE)** \\ \hline Wavelet & 8.17 dB & 0.9173 & 0.6514 \\ \hline DrnRDB & 8.27 dB & 0.8487 & 0.4504 \\ \hline U-Net & 12.99 dB & 0.9638 & 0.3238 \\ \hline
**ARDU** & **14.53 dB** & **0.9772** & **0.2472** \\ \hline \hline \end{tabular} Note: Bold text indicates the best performance.  
\end{table} 
Table 3: Denoising results of seismic signals with an SNR of 6 dB using different methods. Figure 12: Denoising results of seismic signals with an SNR of \(-6\) dB using different methods. Figure 14: Denoising results of seismic signals with an SNR of 6 dB using different methods. Figure 13: Denoising results of seismic signals with an SNR of 0 dB using different methods. It can be seen from Table 3 and Figure 14 that when dealing with seismic signals with an SNR of 6 dB, the ARDU model had the best overall denoising effect of the co-band noise, followed by the U-Net model. Compared to the U-Net model, the SNR and \(r\) of the ARDU model improved by 1.54 dB and 1.39%, respectively, while the SNR and \(r\) did not improve much, but the RMSE reduced by 23.66%, which indicates that the ARDU model can significantly mitigate the problem of signal waveform distortion after denoising and further improve the quality of seismic signals.