# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 6 Conclusions  
Based on the differences between microseismic events and mine blasts, and combined with previous research conclusions, a new discrimination method for microseismic events and blasts is presented in this paper. In this method, 22 seismic parameters were selected as the discrimination feature parameters, and their correlation was analyzed. Then, the artificial intelligence model called PSO-ELM was used to discriminate microseismic events and mine blasts. After the development of the optimal model, this method was applied to the discrimination of microseismic records in Fankou Lead-Zinc Mine and compared with the original ELM and commonly used BPNN, NBC, and FDA to check its performance. The results show that PSO-ELM has the best discrimination performance regarding microseismic events and mine blasts, followed by BPNN, ELM, FDA, and NBC. In addition, the values of all quality measuring indicators of PSO-ELM are the highest. Specifically, the _ACC_, _PPV_, _NPV_, _Sn_, _Sp_ and _MCC_ of PSO-ELM predicting the training set are 0.9844, 0.9890, 0.9799, 0.9797, 0.9891 and 0.9688, respectively. The _ACC_, _PPV_, _NPV_, _Sn_, _Sp_ and _MCC_ of PSO-ELM predicting the testing set are 0.9969, 1, 0.9938, 0.9938, 1 and 0.9938, respectively. The values of these indicators are close to or equal to 1, which is almost \begin{table}
\begin{tabular}{l l c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multicolumn{8}{c}{**Results**} & \multicolumn{8}{c}{**Score**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-14}  & & **ACC** & **PPV** & **NPV** & **Sn** & **Sp** & **MCC** & **ACC** & **PPV** & **NPV** & **Sn** & **Sp** & **MCC** & **Score** \\ \hline \multirow{2}{*}{BPNN} & Training & 0.9672 & 0.9687 & 0.9657 & 0.9656 & 0.9688 & 0.9344 & 4 & 4 & 4 & 4 & 4 & 4 & 4 & 4 \\  & Testing & 0.9500 & 0.9500 & 0.9500 & 0.9500 & 0.9500 & 0.9000 & 0.9000 & 4 & 4 & 3 & 3 & 4 & 4 & 4 \\  & Training & 0.7688 & 0.6933 & 0.9410 & 0.9641 & 0.5734 & 0.5839 & 1 & 1 & 2 & 3 & 1 & 1 & 21 \\  & Testing & 0.8031 & 0.7215 & 0.9802 & 0.9875 & 0.6188 & 0.6522 & 1 & 1 & 4 & 4 & 1 & 1 & 1 \\  & Training & 0.9156 & 0.9080 & 0.9236 & 0.9250 & 0.9063 & 0.8314 & 2 & 2 & 1 & 1 & 2 & 2 & 2 \\  & Testing & 0.9000 & 0.9000 & 0.9000 & 0.9000 & 0.9000 & 0.8000 & 2 & 2 & 2 & 2 & 2 & 2 & 2 \\  & Training & 0.9492 & 0.9528 & 0.9457 & 0.9453 & 0.9531 & 0.8985 & 3 & 3 & 2 & 3 & 3 & 3 & 3 \\ ELM & Testing & 0.9031 & 0.9108 & 0.8957 & 0.8938 & 0.9125 & 0.8064 & 3 & 3 & 1 & 1 & 3 & 3 & 3 \\ PSO- & Training & 0.9844 & 0.9890 & 0.9799 & 0.9797 & 0.9891 & 0.9688 & 5 & 5 & 5 & 5 & 5 & 5 & 5 \\ ELM & Testing & 0.9969 & 1.0000 & 0.9938 & 0.9938 & 1.0000 & 0.9938 & 5 & 5 & 5 & 5 & 5 & 5 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Discrimination results for different discrimination models. Figure 11: Discrimination results for different discrimination models. (**a**) the training set; (**b**) the testing set. perfect and shows that PSO-ELM has outstanding performance. In summary, PSO-ELM has great potential for the discrimination of microseismic events and blasts, and it is of great significance to ensure the safe and smooth operation of mines. Conceptualization, J.Z. ([PERSON]); Investigation, D.R., Z.D. and J.Z. ([PERSON]); Methodology, D.R. and J.Z. ([PERSON]); Project administration, X.S. and Y.G. ; Software, D.R. ; Supervision, X.S. and Z.Y. ; Visualization, D.R. ; Writing--original draft, D.R. ; Writing--review & editing, Z.Y. and Y.G. All authors have read and agreed to the published version of the manuscript. The National Natural Science Foundation Project of China (Grant No. 51874350, [PERSON]). The National Natural Science Foundation Project of China (Grant No. 41807259, [PERSON]). The National Key R&D Program of China (2017 YC0602902, Prof. [PERSON]). The Fundamental Research Funds for the Central Universities of Central South University (2020 zzts200, [PERSON]). The Innovation-Driven Project of Central South University (2020 CX040, [PERSON]). Not applicable.