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Remote Sens. 2018, 10(9), 1444; https://doi.org/10.3390/rs10091444

Method Combining Probability Integration Model and a Small Baseline Subset for Time Series Monitoring of Mining Subsidence

1,* , 2,* and 3
1
Key Laboratory for Land Environment and Disaster Monitoring of NASG, China University of Mining and Technology, Xuzhou 221116, China
2
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
3
Center for Hydrogeology and Environmental Geology Survey, CGS, Baoding 071051, China
*
Authors to whom correspondence should be addressed.
Received: 30 July 2018 / Revised: 1 September 2018 / Accepted: 6 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue InSAR for Earth Observation)
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Abstract

Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) has high accuracy for monitoring slow surface subsidence. However, in the case of a large-scale mining subsidence areas, the monitoring capabilities of TS-InSAR are poor, owing to temporal and spatial decorrelation. To monitor mining subsidence effectively, a method known as Probability Integration Model Small Baseline Set (PIM-SBAS) was applied. In this method, mining subsidence with a large deformation gradient was simulated by a PIM. After simulated deformation was transformed into a wrapped phase, the residual wrapped phase was obtained by subtracting the simulated wrapped phase from the actual wrapped phase. SBAS was used to calculate the residual subsidence. Finally, the mining subsidence was determined by adding the simulated deformation to the residual subsidence. The time series subsidence of the Nantun mining area was derived from 10 TerraSAR-X (TSX) images for the period 25 December 2011 to 2 April 2012. The Zouji highway above the 9308 workface was the target for study. The calculated maximum mining subsidence was 860 mm. The maximum subsidence for the Zouji highway was about 145 mm. Compared with the SBAS method, PIM-SBAS alleviates the difficulty of phase unwrapping, and may be used to monitor large-scale mining subsidence. View Full-Text
Keywords: probability integration model; SBAS; mining subsidence; deformation monitoring probability integration model; SBAS; mining subsidence; deformation monitoring
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Fan, H.; Lu, L.; Yao, Y. Method Combining Probability Integration Model and a Small Baseline Subset for Time Series Monitoring of Mining Subsidence. Remote Sens. 2018, 10, 1444.

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