## 5 Conclusions  
The goal of this study is to use a two-dimensional ResNet-10 model to recognize microseismic waveforms caused by borehole HF in coal mines. The ResNet-10 model is compared to the threshold value, STA/LTA, and expert recognition. Additionally, microseismic waveforms, blasting waveforms, and water flow signal are analyzed in both the time and frequency domains. The following conclusions are drawn:  
1. In terms of accuracy and recall rate, ResNet-10 outperforms the expert and STA/LTA recognition methods. The accuracy rates of STA/LTA, experts, and ResNet-10 in recognizing microseismic events are 84%, 100%, and 100%, respectively. The recall rates of STA/LTA, experts, and ResNet-10 in recognizing microseismic events are 61%, 73%, and 77%, respectively. ResNet-10 can recognize weak waveforms that are difficult for STA/LTA and experts to recognize.
2. Within 30 min of the water pressure dropping, there are 15 water flow signals. According to the test records, water droplets flow out of the surrounding rock during the time range of the water pressure drop. This suggests that when water flow signal occurs, it often corresponds to the penetration of hydraulic cracks and the seepage of water.
3. In the time domain, the duration of water flow signal is 11.534 s, which is much longer than that of microseismic and blasting waveforms. In terms of time-frequency, the frequency band with the highest energy distribution ratio of the blasting waveform is 400-500 Hz, and the energy distribution ratio is 35.5%. The frequency band with the highest energy distribution ratio of microseismic waveforms is 0-100 Hz, and the energy distribution ratio ranges from 60.7% to 99.9%. The frequency band with the highest energy distribution ratio of water flow signal is 0-100 Hz, and the energy distribution ratio is 60.6%.
4. The recognition of more microseismic events, especially weak ones, helps to obtain a more complete fracture propagation trajectory, which can optimize the fracturing process and prevent construction risks. However, how to better match the microseismic waveforms with the model is a difficult task. Next, we will use a more intelligent and more suitable model to complete the classification task of microseismic wavforms.  
Conceptualization, N.L.; methodology, N.L. and Y.Z.; code, Y.Z., field test and validation, N.L.; data analysis, Y.Z. and N.L.; writing--original draft preparation, N.L. and Y.Z.; writing--review and editing, L.S., J.Q., X.H. and Y.L.; project administration, N.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2022 YFE0128300) and the Fundamental Research Funds for the Central Universities (2022 XSCX23).  
Not applicable.  
The data used to support the study are available from the corresponding author upon request ([EMAIL_ADDRESS]). The data are not publicly available due to privacy.  
We would like to thank the editor and anonymous reviewers for their constructive comments. We would like to thank [PERSON] form China University of mining and technology for his guidance and advice on this work. We would like to thank [PERSON] form China University of mining and technology, [PERSON] from HoHai University, [PERSON], [PERSON] and [PERSON] from University of Science and Technology of China, and [PERSON] form University of New South Wales for their help in doing this experiment and data analysis.  
The authors declare no conflict of interest.