LuTan-1 InSAR-Constrained Reconstruction of Concealed Coal-Mining Working-Face Geometry

To address the difficulty in determining underground working face geometry when mining records are incomplete or mining activities are concealed, this study proposes an InSAR-constrained method for inverting the geometric parameters of concealed working faces. The method uses the line-of-sight (LOS) deformation field derived from LuTan-1 InSAR observations as the primary input. The search ranges of mining depth, mining thickness, coal seam dip angle, strike azimuth, working face center coordinates, strike length, and dip length are constrained using the morphology of the subsidence basin, available empirical parameters, and the Probability Integral Method (PIM). These constraints reduce the uncertainty caused by a large search space and parameter coupling in high-dimensional inversion. An adaptive multi-strategy differential evolution algorithm is then introduced. Latin hypercube sampling is used for population initialization. Dynamic parameter adjustment, multiple mutation strategies, reflective boundary handling, and local refinement are incorporated to improve optimization stability in complex parameter spaces. The simulation results show that the proposed method generally outperforms particle swarm optimization, genetic algorithm, evolutionary strategy, and standard differential evolution in inversion accuracy, convergence stability, and robustness to errors. It can reliably recover the main geometric parameters of a predefined working face. In the engineering case study of the 97,315 working face, the inverted active-mining geometry during the LuTan-1 observation period is generally consistent with the corresponding mine records, with a deformation-fitting RMSE of 32.53 mm and relative errors of the main geometric parameters below 7.9%. Parameter sensitivity analysis further shows that the geometric dimensions and center location of the working face have pronounced effects on the LOS deformation field. These parameters should therefore be tightly constrained during inversion. The results demonstrate that the proposed method provides a feasible remote-sensing approach for identifying concealed working faces and estimating goaf geometric parameters in mining areas with incomplete records.