# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
## 6 Conclusions  
This study introduces a method for extracting the eigenvalues of microseismic signals. By comparing the two types of signals, the differences in time-frequency, energy distribution, and fractal characteristics were analyzed. The following conclusions are drawn:  
1. The duration of rock fracture signal was mainly distributed in the range of 0-100 ms, and the maximum amplitude was mainly concentrated in the range of 0-500 mv. The main frequency was mainly distributed in the section above 500 Hz, the main energy band was IMF 1, and the fractal box dimension (\(D\)) below 1.1 accounted for 88% of the samples.
2. The duration of rock blasting events was distributed in the range of above 50 ms, and the maximum amplitude of the signal had no definite range. The main frequency was mainly distributed in the range of 0-500 Hz; the main energy bands were IMF 2, IMF 3, and IMF 4, and the fractal box dimension (\(D\)) was more than 1.25, accounting for 70% of the samples.
3. An automatic analysis and recognition system for microseismic signals based on the analysis of signal eigenvalues is established. The system structure is simple, and the judgment system is simple and clear, which can flexibly change the judgment basis of the system according to the actual situation of different mines and has high judgment accuracy. For the microseismic signal extracted from the mine selected in this paper, the recognition accuracy of rock fracture signal was 94.63%, and that of rock blasting signal was 93.34%. Compared with the single method, the recognition accuracy was improved.
4. The system has the advantages of simple and easy acquisition of signal feature recognition. For different types of mines, it is easy to modify the recognition model according to the actual signal characteristics collected on site, so it has a wide range of applicability. Rock microseismic signals and rock fracture signals represent different change rates of rock mass. Therefore, accurate distinction between rock microseismic signal and rock fracture signal has a wide range of applications in the field of underground mine disaster prevention and control.  
Methodology, J.C., H.L., C.R. and F.H.; formal analysis, J.C., H.L., C.R. and F.H.; investigation, F.H.; resources, J.C. and C.R.; data curation, J.C., H.L., C.R. and F.H.; writing-original draft preparation, J.C., H.L., C.R. and F.H.; writing-review and editing, J.C., H.L., C.R. and F.H.; supervision, J.C. and C.R.; funding acquisition, J.C. and C.R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Natural Science Foundation of China (U1602232).  
Not applicable.  
We would like to thank the editor and anonymous reviewers for their constructive comments. We would like to thank the staff of Dahongshan Iron Mine for their help in signal collection.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Learning and Testing** & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline
**Rock fracture signal recognition accuracy** & 95.24\% & 100\% & 90.91\% & 95.45\% & 88.24\% & 82.35\% & 94.12\% & 100\% & 100\% & 100\% \\ \hline
**Rock blasting signal recognition accuracy** & 94.73\% & 100\% & 94.44\% & 83.33\% & 95.65\% & 95.65\% & 95.65\% & 78.95\% & 100\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: 10 training recognition accuracy statistics.