InSAR Surface Subsidence Monitoring and GA-BP Neural Network Deformation Prediction along Guiyang Metro

Abstract

Aiming at the problem of karst subsidence in the urban construction of Guiyang City, 118 scenes of Sentinel-1A images were processed based on SBAS-InSAR technology, and the surface deformation information of the main urban area of Guiyang City from January 2018 to December 2021 was ob-tained and crossed with the monitoring results of PS-InSAR. For verification, based on this, the settlement analysis of the obvious subsidence area in the research area and the 150 m area along the subway line is carried out, and finally the BP neural network improved by the genetic algorithm is used to predict and analyze the subsidence sequence. The results show that: within the monitoring time range, the surface subsidence rate in the study area is concentrated at −5 mm/a~1 mm/a, and the overall is relatively stable. There is no large-scale obvious subsidence phenomenon in the study area, and there are some obvious subsidence areas, all of which have the phenomenon of mountain excavation, mainly related to human activities. The settlement along the three subway lines is generally stable, and there are 1 or 2 obvious settlement areas in each line; combined with the analysis of optical historical images and urban planning data, these settlements are mainly re-lated to engineering construction. Compared with the standard BP neural network, the improved BP neural network has better performance, and the absolute error and root mean square error are the smallest.