## 3 Results
For instance, in Event 1, only Channel 10 has microseismic waveforms with high SNR, while Channels 1, 8, and 12 have low SNR. Similarly, in Event 10, only Channel 10 has high SNR microseismic waveforms, while Channels 7, 8, and 12 have low SNR. As per the standard, at least four channels should contain microseismic waveforms under the same time window, making it difficult for experts to recognize microseismic events. In Event 2, Channels 6 and 7 contain relatively obvious effective waveforms, which are simultaneously recognized by ResNet-10, experts, and STA/LTA methods. The effective waveforms in Channel 8 and Channel 10 are weak and not recognized by the STA/LTA method. In events that are recognized by STA/LTA, experts, and ResNet-10, it is often observed that the number of channels containing microseismic waveforms with higher SNR is more than three. For instance, in Event 12, the SNR of microseismic waveforms in Channels 1, 2, 3, and 4 is high. Such events can easily meet the standard of having at least four channels containing microseismic waveforms. After a comparative analysis by experts, it was determined that the 10 events in Figure 10 were misrecognized as microseismic waveforms by STA/LTA, and the blue circles represent waveforms that are background noises but misrecognized by STA/LTA as effective waveforms. Further review revealed that there was no microseismic waveform present in these 10 events. The waveform in Channel 10 exhibited significant fluctuations,  
Figure 9: Recognition results of microseismic events by different methods. which caused STA/LTA to mistakenly recognize waveforms in Channels 6, 7, 9, and 10 in Events 1-9 as microseismic waveforms. Event 10 is a signal that interferes with the voltage, and it has only one upward pulse signal. The STA/LTA method wrongly recognizes all channel waveforms in Event 10 as microseismic waveforms. However, it should be noted that the voltage signal can be recognized and eliminated if the short-time window and long-time window length of the STA/LTA are set to be the same. To recognize more weak waveforms induced by borehole HF in coal seam, we tested and found that setting the short-time window and long-time window of the STA/LTA to 60 sampling points and 200 sampling points, respectively, provides the best recognition effect. However, in this case, the voltage signal cannot be recognized. The figure depicted in Figure 11 displays the 5 min continuous waveform found in Channel 7. The microseismic waveforms are the ones produced by microseismic events. If the waveform in Channel 7 is the only one that is similar to the microseismic waveform, it is not considered a microseismic waveform. The figure contains 20 microseismic waveforms, all of which were recognized by CNN. Among these, the waveform independently recognized by experts is marked with a black circle, which is easy to recognize due to its high SNR. Waveforms that were not independently recognized by experts are marked with red circles. After ResNet-10 recognized the waveforms marked with the red circle, experts amplified and compared them and found that there were indeed microseismic waveforms. This type of waveform has a low SNR and is difficult to recognize. Figure 11: Five-minute continuous waveform recognition contained in Channel 7. Figure 10: The microseismic events misrecognized by STA/LTA. Expert recognition is usually based on people's subjective judgment and experience, which may have certain subjectivity and errors. Microseismic waveforms are often complex and diverse, and expert recognition may have difficulty processing these complex data. CNN is considered superior in recognizing microseismic waveforms due to the following reasons:  
1. Capability of feature extraction: Traditional methods require manual design of algorithms which demands a lot of domain knowledge and experience along with repeated attempts to find a good algorithm.