# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 5 Comprehensive Discrimination of Microseismic Waveform Signals  
It was observed that the microseismic signals of rock fracture and rock blasting events are not distinguishable in terms of signal duration and maximum amplitude.  
There are some differences between the microseismic signals of rock rupture and the rock blasting events in the main frequency range, which can be obtained through the energy proportion characteristics of each layer after the eight-layer decomposition by the EEMD and fractal dimension.  
In order to improve the accuracy of signal recognition, the choice BP neural network model, which can be used to deal with the classification of nonlinear signals such as microseismic waveform signals, is widely used and relatively mature to comprehensively discriminate microseismic signals. The operation process of BP neural network is shown in Figure 27 and the input layer node name of the BP neural network is shown in Table 2.  
Figure 26: Fractal box dimension scatter distribution diagram.  
Because there are few analysis variables, the hidden layer of the BP neural network is selected. When the number of hidden layer nodes satisfies the following three empirical formulas at the same time, the number of hidden layer nodes is relatively accurate.  
\[\sum_{i=0}^{n}C_{M}^{i}>k \tag{12}\]  
\(k\)_-number of samples; M-hop count; n-number of input layer neurons; if \(i>M\), so \(C_{M}^{i}=0\)._  
\[M=\sqrt{n+m}+a \tag{13}\]  
\(n\)_-number of input layer neurons; m-number of output layer neurons; a-the constant between \([0,10]\)._  
\[M=\log_{2}n \tag{14}\]  
\(n\)_-number of input layer neurons._  
According to the above formula, the hidden layer M of the BP neural network can be a constant between 9 and 13, the number of cycles is set to 1000 times, and the deviation of data from different nodes is selected. It can be analyzed that the deviation is the smallest when \(M=9\), so the number of hidden layer nodes is selected as 9 nodes.  
By setting the rock fracture signal output as \((1,0)\) and the rock blasting signal output as \((0,1)\), the output result processing method determines the column where the maximum output data is, and the maximum column becomes 1 and the other column becomes 0.  
The time-frequency characteristics (signal duration, maximum amplitude, main frequency), energy distribution characteristics, and fractal feature parameters of the collected 50 rock fracture signals and 50 rock blasting signals are used as the characteristic parameter database of rock fracture and blasting events. In each training, 30 rock fracture signals and 30 rock blasting signals are randomly selected as learning samples by MATLAB, and the remaining data are used as test samples. The weight adjustment method adopts the steepest descent method, and the BP network is used to learn the sample set. After the learning is completed, the remaining 20 rock fracture signals and rock blasting signals are identified. A total of 10 model trainings were performed. The results of each training are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input Layer** & 1 & 2 & 3 & 4 & 5 & 6 \\
**Node Number** & signal duration & maximum amplitude & mean frequency & the signal fractal dimension & IMF1 level energy to the total energy & IMF2-5 level energy to the total energy \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input layer node name.  
Figure 27: BP network structure diagram.  
The BP neural network finally has a judgment accuracy of 94.63% for microseismic signals of rock fracture events and 93.34% for microseismic signals of rock blasting events.