# Research on Seismic Signal Denoising Model Based on DnCNN Network
## 5 Conclusions  
This paper introduces a DnCNN-network-based seismic signal denoising model to tackle the limitations of traditional denoising algorithms, which are often hindered by manually selected features or threshold parameters, leading to suboptimal noise removal. The proposed DnCNN model leverages residual learning and batch normalization to efficiently capture the characteristics of seismic signals. Through rigorous training and testing with a curated dataset, the model has proven its proficiency in noise reduction within seismic signals. Comparative experiments with Wavelet Transform (WT), Empirical Mode Decomposition (EMD), and the DnCNN model on seismic signals across various noise levels demonstrate the superior performance of the DnCNN model. The results indicate that the DnCNN model not only surpasses traditional algorithms in denoising efficacy but also enhances the SNRs of seismic signals without being encumbered by the constraints of manual feature selection or thresholding.  
Future work could explore the integration of the DnCNN model into real-time seismic monitoring systems, as well as the development of hybrid models combining DnCNN with other techniques (such as traditional methods) to achieve even more powerful denoising capabilities.  
Conceptualization, L.D. and J.C.; methodology, L.D.; software, L.W.; validation, L.D. and Y.S.; formal analysis, Y.S.; investigation, J.C.; resources, L.D.; data curation, J.C.; writing--original draft preparation, L.W.; writing--review and editing, L.W.; visualization, Y.S.; supervision, Y.S.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**SNR (dB)**} & \multirow{2}{*}{**Wavelet**} & \multirow{2}{*}{**EMD**} & \multicolumn{3}{c}{**Convolutional**} & \multirow{2}{*}{**DnCNN**} \\  & & & & & **(Auto-Encoder)** & & \\ \cline{2-7}  & **SNR (dB)** & & & & **SNR (dB)** & & **r** \\ \hline \(-6\) & 0.36 & 0.5488 & \(-0.14\) & 0.5907 & 2.98 & 0.6630 & 3.01 & 0.6634 \\ \hline \(-4\) & 1.73 & 0.6533 & 1.74 & 0.6950 & 4.11 & 0.7512 & 4.46 & 0.7511 \\ \hline \(-2\) & 3.02 & 0.7416 & 3.48 & 0.7822 & 5.48 & 0.8224 & 5.98 & 0.8223 \\ \hline \(0\) & 4.24 & 0.8014 & 5.01 & 0.8385 & 7.16 & 0.8829 & 7.52 & 0.8831 \\ \hline \(2\) & 5.79 & 0.8631 & 6.63 & 0.8874 & 8.37 & 0.9029 & 8.74 & 0.9014 \\ \hline \(4\) & 7.23 & 0.9025 & 8.21 & 0.9215 & 9.69 & 0.9244 & 10.02 & 0.9251 \\ \hline \(6\) & 8.95 & 0.9374 & 9.43 & 0.9412 & 11.02 & 0.9416 & 11.38 & 0.9415 \\ \hline \(8\) & 10.57 & 0.9555 & 10.58 & 0.9554 & 12.46 & 0.9548 & 12.73 & 0.9552 \\ \hline \(10\) & 12.17 & 0.9795 & 11.72 & 0.9665 & 13.81 & 0.9671 & 13.96 & 0.9687 \\ \hline Average & 6.01 & 0.8203 & 6.30 & 0.8420 & 8.34 & 0.8678 & 8.64 & 0.8679 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of denoising effects of different denoising algorithms under different SNRs.  
**Funding:** This research was funded by the Fundamental Research Funds for Central Universities, grant number _ZY_20180209; the Self Fund Project of Langfang Science and Technology, grant number 2024011026.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The Stanford Earthquake Dataset can be downloaded from the official website of the Stanford Earthquake Dataset (STEAD). This dataset is a global seismic signal dataset for artificial intelligence, supporting the Python programming language.  
**Conflicts of Interest:** The authors declare no conflicts of interest.