基于HVCE-RBFNN的矿区地表三维形变监测研究

提出一种融合赫尔默特方差分量估计和径向基函数神经网络(HVCE-RBFNN)的三维形变计算方法，结合GNSS和InSAR监测数据，解算甘肃省金昌市金川西二采矿区的地表三维形变场。结果表明，基于HVCE-RBFNN方法获取的三维形变结果精度高于传统方法，其东西向、南北向和垂直向的均方根误差(RMSE)分别为20.85 mm、7.41 mm和34.47 mm，3个方向的最大形变量分别为228 mm、300 mm和193 mm，采空区形变空间分布符合开采沉陷规律。

3D Surface Deformation Monitoring of Mining Areas Based on HVCE-RBFNN Method

We propose a 3D deformation fusion method based on Helmert variance component estimation(HVCE) and radial basis function neural network(RBFNN), and fuse the data of GNSS and InSAR monitoring to obtain the 3D surface deformation field of Jinchuan West Second mining area in Jinchang, Gansu. The results show that the accuracy of 3D deformation fields obtained by HVCE-RBFNN method are higher than that obtained by traditional methods, and the RMSE of east-west direction, north-south direction and vertical direction is 20.85 mm, 7.41 mm and 34.47 mm, respectively. The maximum deformation values in three directions are 228 mm, 300 mm and 193 mm, respectively. The spatial distribution of goaf deformation conforms to the law of mining subsidence.