###### Abstract  
Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the microseismic monitoring of hydraulic fracturing in coal seams. This study proposes a recognition method for weak microseismic waveforms based on ResNet-10 to accurately recognize microseismic events generated by borehole hydraulic fracturing in coal mines. To begin with, the background noise and microseismic signals undergo pre-processing through noise reduction and filtering techniques. The preprocessed data are then fed into the ResNet-10 model, and the model parameters are continuously adjusted while the training and test data are updated. The training process stops when the model accuracy rate and loss function value are greater than 99.9% and less than 0.02 for five consecutive times. The model with the highest accuracy rate is then selected to detect the microseismic waveform. The recognition results of ResNet-10 are compared with the threshold value, STA/LTA, and expert recognition results. Finally, the study analyzes flow signal, blasting, and microseismic waveforms. The recognition accuracy rate and recall rate of ResNet-10 are much higher than those of threshold value and STA/LTA, and better than that of the experts. The results of the study show that ResNet-10 can accurately recognize weak microseismic events that are difficult for the threshold value, STA/LTA, and experts to recognize. When water flow signal occurs, it often corresponds to the penetration of hydraulic cracks and the seepage of water. The waveform recognition results demonstrate that the ResNet-10 method has great potential in recognizing weak microseismic waveforms generated by borehole hydraulic fracturing in coal seams. coal seam; borehole hydraulic fracturing; weak microseismic events; artificial intelligence; ResNet-10; water flow signal +
Footnote †: journal: Applied Sciences
strata, such as tight sandstone and shale, HF in coal seams has a relatively small scale and scope. The development of coal joint cracks, soft structure, and stronger heterogeneity make it more challenging to recognize weak microseismic waveforms induced by HF in coal seams [8]. Therefore, accurately recognizing a large number of microseismic events is an essential prerequisite for the microseismic location of HF in coal seams. The algorithm used for microseismic waveform recognition relies mainly on the characteristics of the signal's frequency, energy, waveform, and travel time [9]. Expert recognition was the first method used for microseismic waveform monitoring. This approach can precisely recognize waveforms with varying signal-to-noise ratios (SNR), but it becomes inefficient and time-consuming when dealing with a large number of microseismic waveforms [10]. Subsequently, various methods have been proposed for waveform recognition, including short and long window, Akaike information criteria (AIC), and waveform similarity-based template matching methods [11; 12; 13]. All of these methods have their pros and cons. The long and short window method is effective in recognizing high SNR waveforms, but its accuracy depends on the setting of the short and long window and threshold, making it less effective in recognizing low SNR waveforms [14]. Some methods based on short-time average/long-time average (STA/LTA) have been proposed [15; 16], which have improved recognition efficiency and accuracy rates but still cannot meet the requirements of actual detection due to their complicated calculation process and increased computational requirements [17]. AIC is a widely used method for accurately picking up the arrival time of waveforms, but its recognition effect still depends on the setting of the time window and waveform SNR, making it difficult to detect low SNR waveforms [18; 19]. Scholars have also proposed some improvement methods based on AIC [20; 21].