# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 4 Comparison with Other Existing Methods  
To further demonstrate the denoising performance, the proposed method is compared with other existing methods on a benchmark that is constructed by combining one clean microseismic waveform and one noise. As shown in Figure 8, the amplitude of the noise is varied for different SNRs. The proposed CNN-denoiser is compared to the high-pass filter and wavelet threshold (WT) filter on the performance of signal denoising. The high-pass filter was designed based on the frequency distribution of clean microseismic signals and noise to achieve the best performance of denoising. In WT filter, the wavelet base of Sym8 was selected, and the seven layers of decomposition and domain value were fixed; the soft threshold was used for microseismic signal denoising. Four measures were employed to compare the methods: the improvements of SNRs between noisy signals and estimated microseismic signals, the correlation coefficient, the changes of the maximum amplitude, and the errors of onset time picking. The Short Term Averaging/Long Term Averaging (STA/LTA) methods for onset time picking was used, and the threshold of the STA/LTA method was set to maximize the accuracy of the onset time picking. Compared with the high-pass filter and WT filter, the proposed CNN-denoiser outperforms SNR improvements with a maximum of 64.85 dB (Figure 9a). The highest correlation coefficient of estimated signals indicates that the WT filter and CNN-denoiser causes smaller waveform distortion during denoising, and the performance of the latter is better than the former. In contrast, the high-pass filter method introduces high waveform distortion, so that the maximum correlation coefficient only reached 0.86 (Figure 9b). CNN-denoiser provides the highest correction coefficient, especially for low SNRs. Figure 9c shows that the absolute value of max amplitude changes of the estimated signals by the high-pass filter and CNN-denoiser are almost similar when the SNR is less than 10 dB. However,  
Figure 8: The noisy signal was formed by superimposing the clean signal with noise. Clean signal and noise were all recorded by the same microseismic monitoring acquisition station in Micang Mountain tunnel (China). Figure 7: Signal-to-noise ratio (SNR) distribution of noisy signals before and after denoising by CNN-denoiser in Zijing tunnel (China). the CNN-denoiser is superior to the high-pass filter in terms of the max amplitude changes of noisy signals with high SNRs, indicating that the max amplitude of the estimated signal is closer to the clean signal in Figure 8. Moreover, the max amplitude change caused by the WT filter is the largest in the test. The STA/LTA method is applied to pick up the onset time of the noisy signal and that denoised by the high-pass filter, WT filter, and CNN-denoiser. As shown in Figure 9d, the accuracy of the onset time picking is significantly improved with the high-pass filter, WT filter and the CNN-denoiser, where the CNN-denoiser outperforms the others for low SRNs. It represents an improvement compared with the STA/LTA methods. Although the semi-synthetic data are used for training the network, the performance of the CNN-denoiser on microseismic signal denoising shows its robustness by maintaining an optimal accuracy. However, for new complex noise or microseismic signal samples, the method may not achieve the current performance, which needs validation in further research. The current estimation of the microseismic signal is based on the output of masks, and thus non-mask prediction will be the direction of future research [29]. The increase in training data can continuously separate the microseismic signal and noise perfectly, which will be the goal of the future research. Further, the combination of traditional methods and the neural network will be explored. Figure 9: Performance comparison between high-pass filter, wavelet threshold (WT) filter and CNN-denoiser.