# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 3 Energy Characteristic Analysis of Microseismic Waveform Signals  
In this section, the band energy is used as the breakthrough point to study the characteristics of the frequency band energy of rock fractures and rock blasting signal waveforms. Furthermore, the ensemble empirical simulation decomposition (EEMD) method is used to analyze rock blasting and rock fracture signals. First, the EEMD is used to process the selected signal. After processing, the set number of the intrinsic mode function (IMF) is obtained. According to the following equation, the energy value, \(E_{i}\left(1,2,3\ \ldots\ \ldots\ \right)\), corresponding to each IMF is calculated. \[E_{i}=\sum\left|IMF_{i}\right|^{2} \tag{1}\]  
\[E_{total}=\sum_{i=1}^{n}E_{i} \tag{2}\]  
\[P_{i}=\frac{E_{total}}{E_{i}} \tag{3}\]  
Through the three equations above, we can calculate the energy value contained in the intrinsic mode function of each frequency band and the ratio of the energy contained in a single intrinsic mode function to the total energy of the signal. \(E_{i}\) denotes the energy of each intrinsic mode function, \(E_{total}\) is the sum of energy contained in all intrinsic mode functions of the original signal, and \(P_{i}\) represents the energy ratio in frequency domain of the signal. The two types of signal data are first denoised using the wavelet threshold and then decomposed using the EEMD. The signal energy value corresponding to each interval decreases due to the increase in decomposition levels and eventually approaches zero as time approaches infinity. Therefore, to ensure that the results after decomposition are easy to analyze, eight-level decomposition is selected for the signal. The energy of eight IMF components and the ratio of the energy to the total energy are calculated. The difference in the main energy intervals of the two types of signals is analyzed for the purpose of signal discrimination. EEMD is used to decompose the denoised signal into 8 layers. After decomposition, a total of 9 different spectrograms of IMF1-IMF8 and remainder are generated, which are arranged from high to low frequency. Corresponding to the IMF1-IMF8 and remainder, a total of nine corresponding frequency band energies are generated. The ratio of energy  
Figure 11: Dominant frequency distribution diagram of two types of signals. of each frequency band to the total frequency band energy represents the proportion of different spectrograms in the denoised signal after decomposition. An original rock fracture event signal is selected and processed by the wavelet threshold denoising method (Symlets8 wavelet was selected, and three layers were selected for the decomposition layer). The processing results are shown in Figure 12. By analyzing the results, it can be deduced that the other signals generated in the process of acquisition are approximately straight lines in the graph after processing, and the signal of the rock fracture event has not changed significantly, which indicates that the effect of the wavelet threshold method is significant. Therefore, after noise reduction, the signal energy value is more representative of the signal energy of the actual event. The signal of a rock fracture event after noise reduction is selected and processed by the EEMD. The spectrum is shown in Figure 13. By analyzing the spectrum, it can be deduced that the waveform displayed in the IMF1 frequency spectrum exhibits the highest resemblance to that after noise reduction; the spectrum of the other frequency bands indicates that the similarity between the waveform and denoised signal gradually decreases until it is almost negligible. A raw rock blasting signal is selected and denoised using the wavelet threshold method.