3D Surface Deformation Monitoring of Mining Areas Based on HVCE-RBFNN Method
Abstract 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.
Key words :
GNSS
InSAR
HVCE-RBFNN
3D deformation
mining subsidence
Cite this article:
ZHOU Wentao,ZHANG Wenjun,MIAO Junyi et al. 3D Surface Deformation Monitoring of Mining Areas Based on HVCE-RBFNN Method[J]. jgg, 2022, 42(5): 520-525.
ZHOU Wentao,ZHANG Wenjun,MIAO Junyi et al. 3D Surface Deformation Monitoring of Mining Areas Based on HVCE-RBFNN Method[J]. jgg, 2022, 42(5): 520-525.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2022/V42/I5/520
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