Optimizing Microseismic Sensor Networks in Mines Using Cramér–Rao Lower Bound Framework

The sensor network layout is one of the most important factors affecting the accuracy and stability of microseismic (MS) source localization. To solve the influence of sensor network layout on positioning accuracy, an innovative sensor network optimization method using Cramér–Rao Lower Bound (CRLB) framework is proposed in this paper. The optimization function of sensor network layout is first constructed by exploiting the framework of CRLB, and then an improved genetic algorithm is used to minimize the optimization function and obtain the optimal sensor network layout. The feasibility and effectiveness of the proposed method are verified by simulation tests, and pencil-lead breaks experiment. Besides, the proposed method is applied in a mine engineering, and the result of the blasting experiments shows that the sensor network optimized by the proposed method can achieve the high location accuracy of 15 m (with the accuracy rate of 4.22%). Furthermore, general rules for sensor network layout are given to guide the sensor placements for routine monitoring systems.