摘要
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。
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.
作者
周文韬
张文君
缪骏懿
申锐
訾应昆
ZHOU Wentao;ZHANG Wenjun;MIAO Junyi;SHEN Rui;ZI Yingkun(School of Environment and Resource,Southwest University of Science and Technology,59 Mid-Qinglong Road,Mianyang 621010,China;Mianyang Science and Technology City Division,National Remote Sensing Center of China,59 Mid-Qinglong Road,Mianyang 621010,China;Sichuan Space Remote Sensing and Smart Mapping Technology Co Ltd,389 Fujin Road,Mianyang 621010,China)
出处
《大地测量与地球动力学》
CSCD
北大核心
2022年第5期520-525,共6页
Journal of Geodesy and Geodynamics
基金
国家重点研发计划(2018YFC150540202)
国家自然科学基金(41871174)。
