Research on the Precise Positioning of Mining Working Faces and an Inversion Method for Characteristic Working Face Parameters Based on a Robust Genetic Algorithm

Accurate positioning of mining working faces and reliable acquisition of their spatial characteristics play a key role in controlling illegal mining, in addition to identifying underlying hazards in coal mine goafs. However, when errors are present in observation deformation data, the traditional approach used to precisely locate rectangular working faces and invert their spatial characteristics cannot be used to accurately describe the properties of mining working faces. To address this issue, based on the relationship between surface deformation and the mining-induced response of the underground working face, in combination with the weighted iterative robust genetic algorithm (GA), we construct a precise positioning method for mining working faces. The simulation test results of this method demonstrate that, compared with the conventional GA, it not only has lower fitting errors (reduced from 73.5 mm to 49.0 mm and from 80.8 mm to 48.8 mm, respectively) but also significantly decreases the maximum relative error in the mining characteristic parameter inversion results. By applying it in the actual case of Guqiao Mine’s 1414 (1) mining working face, the proposed method’s reliability is further validated. The obtained results offer practical support for underground goaf detection and mining supervision efforts.