One spatial geometric characteristic identification method of a coal mine working face based on ground movement and deformation monitoring data

Abstract

ABSTRACT Aiming at the bottleneck problem that plagues illegal mining supervision and the historical investigation of closed/abandoned mine mining, that is, the lack of effective large-scale, fast, accurate, and convenient underground mining space geometric feature recognition technology, this paper proposes a spatial geometric characteristic identification method of a coal mine working face based on ground movement and deformation monitoring data. Combing with the mining subsidence prediction model (probability integral method), the identification method with the working face geometric characteristic parameter B as the parameter and the space movement vectors as the observation values is proposed. And the quantum genetic algorithm for solving the parameters of the observation equation is introduced. The high precision, strong ability of anti-random error, and anti-gross error of the method are proved by the simulation experiments. Finally, the method is used to calculate the characteristic parameters of a real goaf. The obtained results are as follows: m = 3.376 m, α = 4.069°, ϑ = 205.527°, H = 530.844 m, D 3 = 620.844 m, D 1 = 196.375 m, X 0 = 56557.125 m, Y 0 = 31516.063 m, and the fitting error is 96.165 mm. The inversion results can accurately describe the spatial geometric characteristics of the goaf.