Abstract
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.
Highlights
The rockburst is a sudden, violent fracture of rock mass in tunnels and mines, generally caused by failure of highly stressed rock and the rapid instantaneous release of accumulated stain energy [1,2,3]
The results demonstrate that the dominant frequency of the coal mining MS signals is generally lower than that of the blasting signals
Three significant characteristics of the coal mining MS and blasting signals were studied by using fast Fourier transform (FFT), short-term averaging/long-term averaging (STA/LTA), and envelope analysis
Summary
The rockburst is a sudden, violent fracture of rock mass in tunnels and mines, generally caused by failure of highly stressed rock and the rapid instantaneous release of accumulated stain energy [1,2,3]. Ma et al analyzed seven parameters to distinguish between mining MS signals and blasting signals in a phosphorite mine, based on which, two statistical discriminant models were proposed by the Bayesian classifier and the Fisher classifier. Their models were more accurate than traditional methods [16, 17]. A discriminant model for coal mining MS signals and blasting signals, with the help of Fisher linear discriminant method, is established, which are useful to improve the efficiency of automatic discrimination and reduce the workload of artificial discrimination
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