Abstract
Given the complex environment experienced in working mines, the vibration waves produced by processes such as rock fracture in deep formations usually show interference effects when monitored due to other signals, the so-called “clutter” in the signal, which are interfered with the clutter. At the same time, owing to the influence of system noise, the first arrival time and the arrival time difference values of the signals obtained cannot easily be determined accurately. The propagation model for the microseismic signals experienced and the discrimination method used to determine the first arrival wave type can be established using knowledge of the spatial geometry between the sensors used and the seismic source. Thus, the filtering of the actual from the abnormal wave signals is possible. Using the theory of signal cross-correlation in this work, a correction method for the arrival velocity of the first microseismic signal has been proposed and evaluated. By calculating the cross-correlation coefficient of the same source vibration signal and finding the position that corresponds to the maximum value of the cross-correlation coefficient, the arrival time difference between the signals seen in the two channels is obtained. Thus, the key conclusions can be drawn from the experiments carried out: when the signal-to-noise ratio of the original signal is low, the time difference can still be determined with high accuracy. Further, a wave velocity correction criterion has also been proposed, where the velocity correction of the S wave or the R wave can be realized by combining the spatial coordinate information on the blasting point and an algorithm representing the signal cross-correlation to arrival time difference is used.
Highlights
Enhancing the technology of microseismic monitoring in mines is very important, to allow for better forecasting of potential problems and creating an early warning of a possible rock burst disaster, all with a view to improving mine safety
2# and 8#, is less than TLP1i, and the first arrival wave they receive is the P wave. e waveforms recorded by sensor 2# of the above two events are based on the arrival time difference from the signal cross-correlation algorithm, and the value of t1i is all less than TLD1i, where the wave velocity vd is adopted
A propagation model for the microseismic signal received has been established according to the arrival time information and the spatial coordinate information for the sensors used, and the judgment criterion for the first arrival wave type has been established. us, it can be judged that the first arrival wave may be P wave, S wave, R wave or abnormal wave
Summary
Enhancing the technology of microseismic monitoring in mines is very important, to allow for better forecasting of potential problems and creating an early warning of a possible rock burst disaster, all with a view to improving mine safety. E propagation model of the microseismic signals experienced and the discrimination method to determine the first arrival wave can be established according to the spatial geometry between the sensors used and the seismic source. Guo et al [7] have introduced a fast scanning method into the field of microseismic monitoring and established a three-dimensional fast scanning algorithm in a Cartesian coordinate system and used the fast scanning algorithm to calculate the first arrival travel time of a signal from a point source in the single velocity model and horizontally layered model, respectively. Based on the above analysis and according to the arrival time and sensor coordinate information, the wave type information corresponding to the monitoring signal used can be identified, and the wave velocity model used in the subsequent source positioning can be established. When the signal’s first arrival cannot be determined accurately, due to the influence of system noise, the conventional automatic time arrival method or manual method, based on long- and short-time windows, will result in different levels of picking errors in this parameter
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