A Microseismic Location Method Based on BP-GA-GN Hybrid Algorithm

In recent years, with the deepening of mining and tunnel excavation operations, the incidence of rock burst has also increased, prompting people to attracting increasing attention to microseismic monitoring technology. The location algorithm of microseismic events is the core of microseismic monitoring. In this study, a hybrid optimization algorithm, BP-GA-GN, which combines genetic algorithm (GA), BP neural network (BP) and Gauss-Newton method (GN), is introduced. The BP-GA-GN algorithm optimizes the initial weights and thresholds of the BP neural network through GA to avoid local optimum. The BP neural network is used to learn the nonlinear mapping between the sensor arrival time difference and the source position. Combined with the physical model constraints of GN, fine convergence is performed. We prove the robustness of the BP-GA-GN algorithm through a large number of numerical simulations. Compared with the traditional single algorithm, the algorithm shows excellent performance. Subsequently, the high precision and high efficiency of the method are further highlighted in the field data test of mine environment and tunnel environment. The average errors are 0.42 m and 2.54 m, respectively, rendering it a valuable tool for real-time microseismic monitoring. This study overcomes the limitations of traditional positioning methods. The algorithm can achieve high-speed training and high precision, thus significantly improving the early warning effect of rockburst risk.