An Optimization Method for the Station Layout of a Microseismic Monitoring System in Underground Mine Engineering

Sensors (Basel). 2022 Jun 24;22(13):4775. doi: 10.3390/s22134775.

Abstract

The layout of microseismic monitoring (MSM) station networks is very important to ensure the effectiveness of source location inversion; however, it is difficult to meet the complexity and mobility requirements of the technology in this new era. This paper proposes a network optimization method based on the geometric parameters of the proposed sensor-point database. First, according to the monitoring requirements and mine-working conditions, the overall proposed point database and model are built. Second, through the developed model, the proposed coverage area, envelope volume, effective coverage radius, and minimum energy level induction value are comprehensively calculated, and the evaluation reference index is constructed. Third, the effective maximum envelope volume is determined by taking the analyzed limit of monitoring induction energy level as the limit. Finally, the optimal design method is identified and applied to provide a sensor station layout network with the maximum energy efficiency. The method, defined as the S-V-E-R-V model, is verified by a comparison with the existing layout scheme and numerical simulation. The results show that the optimization method has strong practicability and efficiency, compared with the mine's layout following the current method. Simulation experiments show that the optimization effect of this method meets the mine's engineering requirements for the variability, intelligence, and high efficiency of the microseismic monitoring station network layout, and satisfies the needs of event identification and location dependent on the station network.

Keywords: S-V-E-R-V model; method optimization; microseismic monitoring; monitoring efficiency; network layout; underground mine.

Grants and funding

This research was funded in part by the National Natural Science Foundation of China under Grant 41772313, and in part by the Hunan Innovation Platform and Talent Plan Project under Grant 2019SR3001.