Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10

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 micro-seismic 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.