# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 3 Results
### Field Data  
In a real environment, the data acquisition environment for seismic signals is often more complex, facing various interference sources and errors introduced by different equipment. To evaluate the performance of various denoising methods on real data, we use a subset of low signal-to-noise ratio seismic data from the STEAD dataset and a subset of data from the ChinArray dataset as test sets and compare the denoising effects of different models on these field data.  
The average SNR of the original data was 4.119 dB. After denoising, the SNR achieved by DeepDenoiser was 9.865 dB, while ARDU attained an SNR of 11.685 dB. In contrast, the proposed method in this study yielded a significantly higher denoised SNR of 14.531.  
We also compared the denoising effects of the three methods on the earthquake recorded by the ChinArray X1 network at the intersection of Nantou County, Chiayi County, and Kaohsiung County in Taiwan (occurring on 31 December 2012, at 00:03:25, with a magnitude of 4.8 and a depth of 10 km). As shown in Figure 9, We arranged the signals according to the epicentral distance, and took the theoretical arrival time of the P-wave as the zero moment. It is expected that high-quality seismic images can clearly observe the arrival of the P wave and S wave. All three methods effectively improved the quality of the seismic image, making the seismic phases more distinct and enhancing their continuity, demonstrating their effectiveness in suppressing noise to varying degrees. In (b), it can be observed that the denoised results obtained by DeepDenoiser have a lower resolution, with noticeable noise residuals between 1600 and 1800 km from the epicenter. In (c), the denoised results with ARDU are clearer, but compared to the proposed method, there are still noise residuals around 1500 km from the epicenter. Therefore, the proposed method can significantly improve the quality of the seismic profile, making the seismic phases more stable and distinct.  
We also calculated the noise residuals after denoising to compare the three methods' effectiveness in preserving the actual seismic signal amplitudes. The residual of the denoising result is the separated noise, which is supposed to be disorderly and cannot observe the P wave and S wave at all. As shown in Figure 10, apparent seismic phases can be observed in the noise residual of DeepDenoiser, indicating that it has damaged the effective signal while suppressing noise. In contrast, no seismic phases are observable in the noise residual of ARDU and the proposed method, suggesting that they have caused less damage to the true signal.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Floating-Point Operations** & **Number of Parameters** \\  & **(G)** & **(M)** \\ \hline DeepDenoiser & 0.31 & 2.65 \\ ARDU & 6.83 & 40.87 \\ Ours & 6.67 & 17.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of computational cost.  
Figure 9: Noise reduction comparison for ChinArray X1 Network. (**a**) shows the original data. (**b–d**) are the denoising results of DeepDenoiser, ARDU, and the method proposed in this paper, respectively. All seismic signals are arranged according to the epicentral distance. In the denoising result of DeepDenoiser, records with epicentral distances between 1600 and 1800 km have noticeable noise. In the denoising result of ARDU, records around 1700 km epicentral distance have some residual noise. In the denoising result of the proposed method, there is no noticeable residual noise, and the in-phase axis is relatively clear.