Monitoring and analysis of mining 3D time-series deformation based on multi-track SAR data

ABSTRACT

Accurate monitoring of the developing process of a surface subsidence basin is the basis of building damage assessment and surface deformation prediction. In this paper, the Synthetic Aperture Radar (SAR) data of three different imaging geometries, TerraSAR, Radarsat-2, and Sentinel-1A, were exploited. Firstly, two-dimensional (2D) time-series deformation of the surface subsidence basin caused by 15,235 working face mining was obtained based on Multidimensional Small Baseline Subset (MSBAS) technology from 19 December 2015 to 5 March 2016. By comparing vertical deformation with levelling data, it is shown that the root-mean-square error of vertical deformation is 3.2 mm and the standard deviation is 1.9 mm when the ascending-descending track SAR data is available. Otherwise, the root-mean-square error of vertical deformation is 18.1 mm and the standard deviation is 11.6 mm. Because of the low precision of the north–south horizontal movement monitored by the SAR sensor, the vertical deformation acquired by MSBAS technology and the rules of the mining subsidence (horizontal movement is proportional to tilt) were combined to obtain the north–south horizontal movement which was proven to be reliable by comparing the 2D time-series deformation obtained by MSBAS technology. Then, the deformation of the railway in the surface subsidence basin was analysed based on the three-dimensional (3D) time-series deformation. The results show that the subsidence, tilt, and horizontal movement strongly influence the railway in the monitoring period, but will not affect the normal traffic. This experiment lays a technical foundation for preventing the occurrence of mining disasters and verifies the ability to monitor the deformation of buildings and structures by interferometry synthetic aperture radar technology.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The research work is supported by the Fundamental Research Funds for the Central Universities [2018BSCXB15], the Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_1950].