An application of InSAR time-series analysis for the assessment of mining-induced structural damage in Panji Mine, China

Jinchao Li (), Fei Gao and Jiaguo Lu
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Jinchao Li: School of Civil and Hydraulic Engineering
Fei Gao: School of Civil and Hydraulic Engineering
Jiaguo Lu: The 38th Research Institute of China Electronics Technology Group Corporation

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2019, vol. 97, issue 1, No 13, 243-258

Abstract: Abstract Underground coal mining activities are likely to cause changes in surface structure, resulting in damage to buildings, which seriously threaten the safety of life and property of the residents within the mining area. In this paper, the synthetic aperture radar time-series analysis method is combined with the building damage level empirical model to conduct a health assessment of the residential buildings in the Panji mining area of Huainan. Through the processing of 50 Sentinel-1A satellite radar images from April 2015 to May 2018, it is indicated that the residential areas in Huainan Panji Mining Area continued to be affected by underground coal mining during this period, and severe settlement occurred. Large areas of buildings in residential areas were damaged. Until May 2018, 202,000 m2 of buildings in the residential area of Panji mining have reached the Class II damage and 41,575 m2 have reached the Class III damage. After field investigation, the actual damage situation of the building is in good agreement with the results of this paper. Therefore, synthetic aperture radar time-series analysis combined with building damage level empirical model can be used as an effective tool for rapid and efficient deformation monitoring and health assessment of buildings. It is of great significance for the identification, early warning and disaster prevention decision making of such disasters.

Keywords: InSAR time-series analysis; Building health assessment; Mining subsidence; Disaster early warning; Sentinel-1A (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11069-019-03639-8

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