准确提取地表形变信息对于预防和控制煤矿开采导致的地质灾害至关重要。以山东郭屯煤矿某工作面为例，首先获取了工作面开采时间段内（2017年7月31日至2018年5月3日）的18景Sentinel-1A卫星影像，基于SBAS-InSAR技术处理得到工作面采空区地表形变。在InSAR观测数据的驱动下，通过推导概率积分法与SBAS-InSAR视线向形变三维参数之间的函数映射关系，提出了一种基于随机误差消除遗传算法的三维全参数反演模型。基于该方法，准确反演了研究区地表沉降参数，通过与现场经验值对比，各参数的偏差均小于3%，拟合精度较高。最后，基于反演参数与概率积分法获得了采空区全盆地沉降形变信息，其中A测线与F测线的均方根误差分别为0.083 m和0.102 m，平均绝对误差分别为0.068 m和 0.089 m，预计结果与实测水准测量数据高度一致，表明所提出的三维全参数反演模型能够以低成本的方式有效获取煤矿采空区全盆地沉降信息。

Accurately extracting surface deformation is essential for the prevention and control of geological hazards caused by underground coal mining. By taking a working face in Guotun Coal Mine, Shandong Province, as the case study, this paper first obtains 18 Sentinel-1A satellite images during the extraction period of the working face (July 31, 2017, to May 3, 2018), and derives the surface deformation of the goaf area based on SBAS-InSAR technology. Then, driven by InSAR observations, the functional projection relationships for the three-dimensional parameters between the probability integration method (PIM) and line-of-sight (LOS) deformation derived by SBAS-InSAR are deduced, and a three-dimensional and full-parameter inversion model based on genetic algorithm with random error elimination (GAREE) is proposed. Based on this model, the subsidence parameters inside the study area are accurately retrieved with the deviation for each parameter less than 3% compared with the empirical parameters. Finally, by using the retrieved parameters, PIM is employed to predict the whole goad deformation with the predicted results highly consistent with the field leveling data. The root mean square errors (RMSE) on observation line A and line F are 0.083 m and 0.102 m, respectively, and the mean absolute errors (MAE) are 0.068 m and 0.089 m, respectively. Results show that the parameter inversion model proposed by this study can effectively obtain the subsidence information for the whole basin of a mining goaf in a low-cost way, providing scientific and significant importance for engineering application and potential disaster predictions in coal mining areas.