## 3 Results  
During the detection of continuous half-hour data, 101 microseismic events were recognized using a threshold value, 95 were recognized using the STA/LTA method, 95 were recognized independently by experts, and 100 were recognized using ResNet-10. Based on these results, the expert reviewed and recognized all the real microseismic events within the half-hour time frame. As a result, 130 real microseismic events were recognized. The accuracy (\(P_{e}\)) and recall rates (\(R_{e}\)) of the four methods were compared and analyzed by the experts. Pe and Re are defined as  
\[P_{e}=T_{p}/(T_{p}+F_{p}), \tag{14}\]  
\[R_{e}=T_{p}/(T_{p}+F_{n}) \tag{15}\]  
In Equations (14) and (15), \(T_{p}\) represents the true example, which means that the microseismic event recognized by the model is the actual microseismic event. If it is a false positive example, then it is represented by \(F_{p}\). Similarly, \(T_{n}\) represents the true counterexample, which means that the background noise recognized by the model is the actual background noise. If it is a false counterexample, then it is represented by \(F_{n}\). A high accuracy rate indicates a low false recognition rate, whereas a high recall rate implies that the model misses a low recognition rate. For the model to be practically useful, both accuracy and recall rates need to be high. Figure 8 shows the statistical results of threshold values, STA/LTA, experts, ResNet-10 methods for identifying effective events. Out of the 101 events that were recognized using a threshold value, only 62 were confirmed to be real, while 39 were wrongly recognized. Meanwhile, 68 real events were wrongly recognized as background noise. When using STA/LTA, 95 events were recognized, of which 80 were real and 15 were misjudged. In total, 50 real events were wrongly recognized as background noise. All 95 events recognized by experts were real events and 35 were recognized as background noise. The accuracy of the threshold value and STA/LTA methods to recognize microseismic events was only 61% and 84%, respectively. However, the accuracy of the expert method and ResNet-10 to recognize microseismic events was 100%. The recall rates of the threshold value, STA/LTA, expert, and ResNet-10 methods to recognize microseismic events were 48%, 61%, 73%, and 77%, respectively. Although none of the four methods could recognize all the real microseismic events, ResNet-10 had the highest recall rate. It was found that ResNet-10 is better than STA/LTA and threshold recognition methods in recognizing microseismic events, and better than expert recognition methods. Due to the poor recognition effect of the threshold value method, it will not be discussed further. Figure 8: Results of microseismic event recognition by 4 methods. Figure 9 shows a total of 12 microseismic events, all of which were accurately recognized by ResNet-10. However, STA/LTA failed to recognize Event 1, Event 2, Event 7, and Event 10, which were also not recognized by the experts. In contrast, STA/LTA, experts, and ResNet-10 all accurately recognized Event 2, Event 3, Event 4, Event 5, Event 6, Event 8, Event 9, Event 11, and Event 12. Black circle represents the waveform recognized by ResNet-10, red circle represents the waveform recognized by experts, and blue circle represents the effective waveform recognized by STA/LTA. It has been observed that the number of channels with high SNR microseismic waveforms, in events that have not been recognized by STA/LTA, is usually less than four. In the seventh event, Channels 6, 7, and 10 have microseismic waveforms with high SNR. However, the microseismic waveform in Channel 8 has a low SNR and is not recognized by STA/LTA. Due to this, the event standard of having at least four channels containing microseismic waveforms is not met. It is often difficult for experts to recognize microseismic events when the number of channels containing high SNR microseismic waveforms is less than three.