# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 2 Time-Frequency Characteristic Analysis of Microseismic Waveform Signal
### Frequency Domain Analysis of Microseismic Signals  
In this section, the frequency domain characteristics of rock fracture and rock blasting signals are analyzed, and the differences between these two types of signals in the frequency domain are studied. The typical signal waveform of the original rock fracture signal is shown in Figure 4, and the typical wave waveform of the original rock blasting signal is shown in Figure 5, but there are some original rock blasting signal waveforms similar to the original rock fracture signal waveforms shown in Figure 6. Therefore, it is necessary to process the original signal waveform in order to better distinguish the difference between the two types of signals in microseismic wave shape. Figure 4: Original signal waveform of rock fractures from 40# sensor on 5 July 2019 (randomly selected rock fracture events). Figure 3: **(a)** Maximum amplitude distribution of two types of signals; **(b)** proportion diagram of maximum amplitude values of two types of signals. To analyze the frequency domain of rock fracture time signals more accurately, it is necessary to first denoise them. Figure 7 shows a comparison between the frequency domain of the signal after noise reduction and the frequency domain of the original signal. Symlets8 wavelet was selected for noise reduction, and three layers were selected for the decomposition layer. Figure 5: Original signal waveform of rock blasting at 33# sensor on 9 July 2019 (randomly selected rock blasting events). Figure 6: Waveform diagram of original signal of rock blasting by 33# sensor on 2 July 2019 (rock blasting event signal similar to rock fracture event signal). The Hilbert-Huang transform (HHT) is a signal analysis method [48; 49; 50; 51] for nonlinear and nonstationary signals similar to microseismic waveform signals. This method has been applied to the processing and analysis of microseismic signals and has achieved good results [52; 53; 54]. Recently, [PERSON] et al. [55] used HHT to analyze the acoustic emission waveform characteristics of rock under uniaxial loading and achieved good results. The HHT was used to process the original and denoised signals to obtain the marginal spectrum of the original and denoised signals, respectively. The marginal spectra of the two signals are shown in Figure 8a,b, respectively. In the marginal spectrum, the corresponding interval of the signal peak value after processing was the main frequency value of the signal. By comparing Figure 8a,b, it can be deduced that after the noise reduction signal was processed, obtaining the signal with only one peak in the main spectrum became easier. Analysis of the original signal before denoising is necessary. Figure 8: (**a**) The marginal spectrum of the original signal; (**b**) the marginal spectrum of the denoised signal. Figure 7: Contrast diagram of noise reduction results. Therefore, for two rock blasting signals selected from the same sensor on different dates, the symlets8 wavelet base was first selected, the original signal was decomposed to three layers, and the HHT was used to process the denoised signal. The noise reduction results and marginal spectrum are shown in Figures 9 and 10, respectively. The results of a comparative analysis of Figures 9 and 10 are summarized as follows. The main frequency of the original signal was not affected by the wavelet-based denoising method; that is, after using HHT to convert the noise-reduced and non-noise-reduced signals, it was found that the main frequency of the signal is clearer after noise reduction on the premise of ensuring that the main frequency characteristics of the signal remain unchanged. To distinguish the two types of signals in the main frequency, HHT was applied to all 50 sets of burst event and blasting event signals. The main frequency was divided into five intervals. Table 1 is the statistical table of the interval where the main frequency of each event was located. Figure 11 shows the main frequency distribution of the two types of signals after statistics.