# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
###### Abstract  
The microseismic signals of rock fractures indicate that the rock mass in a particular area is changing slowly, and the microseismic signals of rock blasting indicate that the rock mass in a particular area is changing violently. It is of great significance to accurately distinguish rock fracture signals and rock microseismic signals for analyzing the changes in the rock mass in the area where the signal occurs. Considering the microseismic signals of the Dahongshan Iron Mine, the time domain, frequency domain, energy characteristic distribution, and fractal features of each signal were analyzed after noise reduction of the original signal. The results demonstrate that the signal duration and maximum amplitude of the signal could not accurately distinguish the two types of signals. However, the main frequency of the rock fracture signal after noise reduction is distributed above 500 HZ, and the main frequency of the rock blasting signal is mainly distributed below 500 HZ. After the denoised signal is decomposed by the ensemble empirical simulation decomposition, the energy of the IMF1 frequency band of the rock fracture signal occupies an absolute dominant position, and the sum of the energy of the IMF2-IMF4 frequency bands of the rock blasting signal occupies a dominant position. The fractal box dimension of the rock fracture signal is mainly below 1.1, and the fractal box dimension of the rock blasting signal is mainly above 1.25. According to the above research results, an automatic signal recognition system based on the BP neural network is established, and the recognition accuracy of the rock blasting and rock fracture signals reached 93% and 94% respectively, when this system was used. ground pressure monitoring; microseismic signal; signal recognition; energy characteristic; fractal dimension +
Footnote †: journal: Medical  
[PERSON] [7] used multiple parameters in microseismic signal events as feature vectors and used support vector machines (SVM) to judge the category of microseismic signal events and achieved high accuracy. [PERSON] et al. [8] used a model based on genetic algorithm optimization to establish a BP neural network to analyze the stress changes during the installation of caissons with multiple influencing factors and achieved good results. It is proved that this method can analyze the complex situation of multiple factors. In previous studies, most of them used a single-feature qualitative analysis (such as a single selection of certain aspects of the characteristics) or selected some waveform parameters as recognition features rather than using a unified analysis of multiple aspects of the characteristics. Although previous feature extraction methods can still perform preliminary identification, they are often not extensive. Many scholars who study microseismic signals mostly extract the characteristic parameters of microseismic signals by Fourier transform, wavelet transform, and wavelet packet decomposition and combine them with other pattern recognition methods to achieve waveform discrimination. However, the nonlinear and nonstationary characteristics of microseismic event signals are often ignored. The above several eigenvalue extraction methods are all aimed at the traditional stationary signal processing methods. When using the analysis method for processing stationary signals to process nonstationary signals, it may often cause errors. [PERSON] et al. [9; 10] and [PERSON] et al. [11] used a comprehensive analysis of multiple parameters and established an analysis method for identification models in the analysis of tailings pond accumulation characteristics, tailings dam saturation lines, tailings flow evolution models, tailings dam breaks, and other complex situations containing multiple different parameters. Compared with the in-depth analysis of a certain parameter, comprehensive analyses of multiple parameters and the analysis of the correlation between parameters are carried out.