Abstract: In order to improve the positioning accuracy of mining-induced seismicity monitoring system, reduce the monitoring blind area and reduce the monitoring cost, based on the distributed idea, this paper proposes an positioning method of mining-induced seismicity based on smart phone sensor network. Firstly, smart phones used by workers and their families near the mining area were used to establish a mobile sensor network. Secondly, the simulated source points were meshed and the objective function based on standard deviation was constructed. An improved firefly optimization strategy was proposed. The inflection point backtracking method and smart phone sensor network exclude discrete points strategy, namely EDPS to reduce positioning error. Finally, it is verified by the simulation experiment of mining-induced seismicity location. The experimental results show that: Under the ideal condition that there is no arrival time error in the smart phone sensor network, all the simulated source points can converge to the source position accurately, and the positioning error is less than 1 m. However, compared with the detector, the arrival error of smart phone is higher, and the positioning error is correlated with the arrival error. When the mobile phone arrival error is - 1.0s ~ 1.0s, the traditional algorithm positioning error is 216m, which can not achieve high-accuracy positioning. By researching the relationship between the objective function value and positioning error, propose and use two optimization methods, inflection point backtracking method and EDPS. The absolute positioning error of the algorithm is reduced to 73m. When the time error is -0.2s ~ 0.2s, the absolute positioning error is reduced to 17m, and the positioning accuracy is improved by 76.1%. Location method of mining-induced seismicity based on crowd-sensing of phone mobile sensor network provides a new method for mining-induced seismicity monitoring. It can be considered to combine with underground microseismic system in the future, which is great significance in saving monitoring cost and improving positioning accuracy.