An Improved GNSS and InSAR Fusion Method for Monitoring the 3D Deformation of a Mining Area

Abstract

Accurate measurement of the surface three-dimensional deformation fields in a mined-out area is essential for understanding the law of mining subsidence and guiding the safe production and construction of mining areas. In this study, we proposed an improved fusion method to monitor this three-dimensional deformation. We used the Helmert variance component estimation (HVCE) method and the back-propagation neural network (BPNN) to fuse the global navigation satellite system (GNSS) and interferometric synthetic aperture radar (InSAR) measurements. Based on this improved fusion method, we measured the three-dimensional deformation in the West Second Mining Area of Jinchuan, Jinchang City, Gansu Province, China, from March 22, 2019, to June 8, 2020. The root mean square errors (RMSEs) based on our method for the three directions of east-west (E-W), north-south (N-S), and up-down (U-D) were 21.4 mm, 7.6 mm, and 34.2 mm, respectively. These RMSE values are lower than those obtained from previous methods. The results show that the spatial distribution of the three-dimensional deformation follows the law of mining subsidence.

Keywords

• GNSS
• InSAR
• HVCE-BPNN method
• three-dimensional deformation
• mining subsidence