Subsidence monitoring using D-InSAR and probability integral prediction modelling in deep mining areas

Abstract

Land subsidence processes in deep mining areas have long time durations, and land deformation models should be obtained using many field observations. In this paper, the capability of monitoring deep mining subsidence of ALOS PALSAR pairs with short and long time baselines has been investigated in the area of Xuzhou, Jiangsu province. For the image pairs with poor temporal baselines, it is difficult to correctly generate the whole subsidence basin, and more information is lost in the areas that have rapid changes in deformation and vegetation. Therefore, an approach combining differential interferometric synthetic aperture radar (D-InSAR) results and probability integral model (PIM) results, to generate the whole mining subsidence basin, is proposed. D-InSAR-derived subsidence observations are used to deduce prediction parameters, and then the parameters and mining conditions of working faces are used in a probability integral model to obtain the whole subsidence basin. The results are compared with levelling field survey data, and the prediction results and levelling measurements agree well with each other.

Keywords:

• D-InSAR
• Mining subsidence
• Probability integral model
• Prediction

Acknowledgements

The research work has been funded by the Basic Research Project of Jiangsu Province (Natural Science Foundation) (no. BK20130174), the Special fund for surveying, mapping and geoinformation scientific research in public interest (no. 201412016), the Natural Science Foundation of China (no. 41272389), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.