To study the near-field seismic impact of coal fractures in stress concentration zones, we established a source generation model based on finite dislo

Generally, there are two important types of microseismic (MS) signals caused by mining and blasting activities at coal mines. The waveform characteristics of MS signals using FFT, STA/LTA method, and envelope analysis were studied to distinguish these two types of MS signals. The main results are as follows: the dominant frequency and duration of two types of signals are significantly different. The following peak envelope curves of two types of MS signals fit a power function. The power exponent was obtained to describe the attenuated speed of the MS signals. The attenuation of the coal mining MS signals is slower and more fluctuant than that of the blasting signal. Waveform characteristics consisting of the dominant frequency, duration, and attenuation coefficient were extracted as the discriminating parameters. The discriminating performance of these parameters was compared and discussed. Based on the waveform characteristics, a discriminant model for coal mining MS and blasting signals was established by using Fisher linear discriminant method and its performance was checked. The results show that the accuracy of the discriminant model is more than 85%, which can meet the requirements of MS monitoring at coal mines.

Hydraulic fracturing (HF) is an effective technology to prevent and control coal dynamic disaster. The process of coal hydraulic fracturing (HF) induces a large number of microseismic/acoustic emission (MS/AE) waveforms. Understanding the characteristic of AE waveforms’ parameters is essential for evaluating the fracturing effect and optimizing the HF strategy in coal formation. In this study, laboratory hydraulic fracturing under true triaxial stress was performed on a cubic coal sample combined with AE monitoring. The injection pressure curve and temporal variation of AE waveforms’ parameters in different stages were analyzed in detail. The experimental results show that the characteristics of the AE waveforms’ parameters well reflect the HF growth behavior in coal. The majority of AE waveforms’ dominant frequency is distributed between 145 and 160 kHz during HF. The sharp decrease of the injection pressure curve and the sharp increase of the AE waveforms’ amplitude show that the fracture already runs through the coal sample during the initial fracture stage. The “trapezoidal” rise pattern of cumulative energy and most AE waveforms with low amplitude may indicate the stage of liquid storage space expansion. The largest proportion of AE waveforms’ energy and higher overall level of AE waveforms’ amplitude occur during the secondary fracture stage, which indicates the most severe degree of coal fracture and complex activity of internal fracture. The phenomenon shows the difference in fracture mechanism between the initial and secondary fracture stage. We propose a window-number index of AE waveforms for better response to hydraulic fracture, which can improve the accuracy of the HF process division.