Abstract: An online stress monitoring system is used to obtain a stress distribution of each position, a geophone monitoring system is used to monitor high-frequency vibration signals produced by coal and rock fracture, a support resistance monitoring system is used to monitor working resistance of hydraulic supports in a working face, a well-ground joint microseismic system is used to monitor low-frequency vibration signals produced by the coal and rock fracture, and realize position of a microseismic source and inversion imaging of mining fractures, and an anchor stress monitoring system is used to monitor stress states of anchor bolts and cables. A data processing center is used to comprehensively process data obtained from the above five systems using an early warning model, thereby to obtain a prediction result of rock burst and determine whether to make early warning, thus workers can take timely measures according to the early warning situation.

Abstract: A method for improving a monitoring capability of a borehole-surface micro-seismic monitoring system includes selecting multiple candidate points for installing surface wireless sensors to form a natural-number-coded candidate point set and combining a fixed number of candidate points randomly selected from the candidate point set with an underground installed sensor set to form a borehole-surface micro-seismic monitoring network; carrying out multiple random selections until a certain scale of borehole-surface micro-seismic monitoring network deployment plans are generated; establishing an evaluation model for a monitoring capability of each borehole-surface micro-seismic monitoring network deployment plan according to a propagation relation equation between a micro-seismic energy and a first-arrival peak amplitude of a P-wave, forming an initial population; determining an optimal borehole-surface micro-seismic monitoring network deployment plan through a genetic algorithm; and determining an optimal surface

Abstract: A timing alignment method for data acquired by monitoring units of a borehole-surface micro-seismic monitoring system includes acquiring two rock-burst waveform data segments with GPS timestamps; calculating a time difference and a number of sampling points between each pair of adjacent GPS timestamps; adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp between each pair of adjacent GPS timestamps; calculating average sampling frequencies of the two rock-burst waveform data segments, adding, on an equal-interval basis, a sampling time to a sampling point missing a timestamp except first and last GPS timestamps in each of the two data segments; obtaining sampling times of all sampling points, resampling the sampling times according to a uniform sampling frequency; calculating a rock-burst waveform data segment at a new sampling time with a linear interpolation formula, and aligning the sampling times of the two rock-burst waveform data segments.

Abstract: A rock burst hazard prediction method based on vibration wave energy attenuation characteristics of a mine earthquake cluster is provided. The rock burst hazard prediction method comprehensively considers the static load and dynamic load effects of the vibration waves of the mine earthquake cluster based on vibration wave energy attenuation characteristics of the mine earthquake cluster. The static load strength index and the dynamic load strength index involved in the method have clear physical meanings. A comprehensive prediction index calculation model proposed based on the dynamic and static load superposition principle of rock burst occurrence is clear, and the method has a firm theoretical support as well as strong universality and operability. Meanwhile, the updating and adjustment of weights are rapid and the objective judgment and prediction of the final comprehensive prediction results are efficient, and the high-energy mine earthquake and impact behavior area can be effectively predicted.