CNN-KA: A Hybrid P-Phase Picking Method for Microseismic Source Location in Deep Mine With Complex Geological Conditions

Microseismic (MS) technology is one of the effective methods for engineering fracture monitoring and has been widely used in coal mining. The accurate P-phase arrivals are critical for improving the accuracy of source locations and MS parameters. In this study, a multistep method (CNN-KA) combining convolutional neural network (CNN) combined, K-means and Akaike information criterion (AIC) is proposed. First, CNN is applied to recognize mining-induced waveforms of rock fractures. Second, the maximum overlapping discrete wavelet transform and multiresolution analysis (MRAs) are integrated to denoise for acquired waveforms. Subsequently, a new picker was developed by introducing K-mean clustering to AIC. Finally, the performance of the proposed CNN-KA was evaluated with open access and in-field data. Combined with software development, the proposed method was applied in 12123 working face of Paner coal mine in Anhui Province, China. The results show that the accuracy of CNN-KA in recognizing mining-induced rock fracturing events is 98.11%. The mean absolute error (MAE) of CNN-KA is 0.0915 s at 200-Hz frequency, which is 86.65% lower than STA/LTA (short-term average to long-term average (STA/LTA), one of the most commonly used in MS technology). The errors of picking and positioning are 3.7 ms and 10.89 m, respectively, at 5000-Hz frequency. MS source parameters based on CNN-KA were effectively used to interpret the abnormal dynamic response of the roof stratum in 12123 working face of Paner coal mine, which was confirmed by real-time mine pressure and design data of the residual coal pillars. The results can be further used for calculating precise MS source parameters and early warning of engineering geohazards.