# Seismic Data Denoising Based on Wavelet Transform and the Residual Neural Network
## 3 Synthetic Seismic Data Processing Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input SNR(dB)** & \multicolumn{6}{c}{**18.074**} \\ \hline Wavelet decomposition level & haar & db5 & sym4 & coif3 & bior1.5 & rbio3.1 \\ Wavelet name & & & & & & \\ \hline Level 1 output SNR(dB) & 26.390 & 26.761 & 26.836 & 27.184 & 26.734 & 27.151 \\ Level 2 output SNR(dB) & 27.679 & 28.551 & 28.608 & 28.229 & 27.791 & 28.418 \\ Level 3 output SNR(dB) & 26.828 & 26.971 & 27.004 & 26.196 & 26.794 & 28.875 \\ Level 4 output SNR(dB) & 26.218 & 25.899 & 26.236 & 26.558 & 26.622 & 28.991 \\ Level 5 output SNR(dB) & 26.426 & 26.314 & 26.371 & 26.496 & 26.698 & 28.983 \\ \hline Input SNR(dB) & \multicolumn{6}{c}{4.074} \\ \hline Wavelet decomposition level & haar & db8 & sym7 & coif4 & bior6.8 & rbio3.1 \\ Wavelet name & & & & & & \\ \hline Level 1 output SNR(dB) & 19.365 & 19.105 & 18.923 & 19.582 & 19.161 & 19.230 \\ Level 2 output SNR(dB) & 20.494 & 20.897 & 20.700 & 20.684 & 20.575 & 21.337 \\ Level 3 output SNR(dB) & 20.279 & 21.491 & 21.213 & 21.363 & 21.621 & 21.466 \\ Level 4 output SNR(dB) & 19.997 & 20.965 & 21.060 & 20.940 & 20.686 & 21.345 \\ Level 5 output SNR(dB) & 19.875 & 21.121 & 20.547 & 20.806 & 20.619 & 21.298 \\ \hline Input SNR(dB) & \multicolumn{6}{c}{\(-2.756\)} \\ \hline Wavelet decomposition level & haar & db4 & sym7 & coif2 & bior2.6 & rbio6.8 \\ Wavelet name & & & & & & \\ \hline Level 1 output SNR(dB) & 15.734 & 15.572 & 15.712 & 15.448 & 15.618 & 15.795 \\ Level 2 output SNR(dB) & 17.339 & 17.652 & 17.416 & 17.542 & 17.473 & 17.588 \\ Level 3 output SNR(dB) & 17.086 & 17.899 & 17.882 & 17.943 & 17.751 & 18.035 \\ Level 4 output SNR(dB) & 15.517 & 16.024 & 16.392 & 16.187 & 16.047 & 16.280 \\ Level 5 output SNR(dB) & 15.221 & 15.920 & 15.998 & 15.826 & 15.704 & 15.799 \\ \hline Input SNR(dB) & \multicolumn{6}{c}{\(-5.467\)} \\ \hline Wavelet decomposition level & haar & db7 & sym7 & coif3 & bior1.5 & rbio4.4 \\ Wavelet name & & & & & \\ \hline Level 1 output SNR(dB) & 13.946 & 13.861 & 13.923 & 13.971 & 14.078 & 14.051 \\ Level 2 output SNR(dB) & 15.316 & 15.569 & 15.400 & 15.579 & 15.401 & 15.530 \\ Level 3 output SNR(dB) & 15.114 & 16.059 & 16.131 & 15.804 & 15.781 & 15.941 \\ Level 4 output SNR(dB) & 13.095 & 13.624 & 13.589 & 13.820 & 13.573 & 13.764 \\ Level 5 output SNR(dB) & 13.033 & 13.704 & 13.018 & 13.366 & 12.847 & 13.052 \\ \hline Input SNR(dB) & \multicolumn{6}{c}{\(-7.512\)} \\ \hline Wavelet decomposition level & haar & db5 & sym8 & coif5 & bior6.8 & rbio4.4 \\ Wavelet name & & & & & \\ \hline Level 1 output SNR(dB) & 13.113 & 12.821 & 12.972 & 13.043 & 13.104 & 12.990 \\ Level 2 output SNR(dB) & 13.743 & 13.903 & 13.943 & 14.014 & 14.015 & 14.037 \\ Level 3 output SNR(dB) & 13.444 & 14.321 & 14.296 & 14.233 & 14.338 & 14.264 \\ LevelPrevious experiments were conducted under Resnet18, but as to whether Resnet18 can achieve optimal denoising results is yet to experimented with. Therefore, taking the seismic data with the SNR of 4.074 dB as an example, we verified the denoising effect of the Resnet network under different layers by increasing or decreasing the number of layers of Resnet18 with a 3-level wavelet decomposition using sym7 (Figure 3). One convolutional layer and four layers of two convolutional residual blocks make up the Resnet18 architecture shown in Figure 1. In this thesis, the increase or decrease of the number of layers in the Resnet18 network is concentrated on the layers with residual blocks. Resnet14 means removing any layer of Resnet18. When the number of Resnet layers is 14, no matter which layer is removed, the denoising effect will be worse, especially the removal of layer4, which leads to the worst denoising result, only 18.483 dB; while layer16 is the removal of one of the residual blocks in any layer when the number of Resnet layers is 16, the removal of one residual block in layer4 has the greatest effect on the network denoising.