To determine the relationship between underground mining, groundwater storage change, and surface deformation, we first used two sets of ENVISAT data and one set of Sentinel-1A data to obtain surface deformation in eastern Xuzhou coalfield based on the temporarily coherent point interferometric synthetic aperture radar (TCPInSAR) technique. By comparison with underground mining activities, it indicated that the surface subsidence is mainly related to mine exploitation and residual subsidence in the goaf, while the surface uplift is mainly related to restoration of the groundwater level. The average groundwater storage change in the eastern Xuzhou coalfield from January 2005 to June 2017 was obtained through the Gravity Recovery and Climate Experiment (GRACE) data, and the results indicated that the groundwater storage changed nonlinearly with time. The reliability of the groundwater monitoring results was qualitatively validated by using measured well data from April 2009 to April 2010. Combining with time of mining and mine closing analysis, groundwater storage change within the research area had a strong correlation with drainage activity of underground mining. An analysis was finally conducted on the surface deformation and the groundwater storage change within the corresponding time. The results indicated that the groundwater storage variation in the research area has a great influence on the surface deformation after the mine closed.

Mining goafs can cause many hazards, such as burst water, spontaneous combustion of coal seams, surface collapse, etc. In this paper, a feature-points-based method for the efficient location of mining goafs is proposed. Different interferometric synthetic aperture radar (DInSAR) is used to monitor the subsidence basin caused by mining. Using the principles of the probability integral method (PIM), the inflection points and the boundary points of the basin monitored by DInSAR are determined and used as feature points to locate the goaf. In this paper, the necessity of locating goafs and the traditional methods used for this task are discussed first. Then, the results of verifying the proposed method by both a simulation experiment and real data experiment are presented. Six RADARSAT-2 images from 13th October 2015 to 5th March 2016 were used to acquire the subsidence basin caused by the 15235 working faces of the Jiulong mining area. The average relative errors of the simulation experiment and real data experiment were about 6.43% and 12.59%, respectively. The average absolute errors of the simulation experiment and real data experiment were about 28 m and 38 m, respectively. In the final part of this paper, the error sources are discussed to illustrate the factors that can affect the location result.

Economic development and population expansion in mining cities have led to a growing scarcity of land for construction and thus the closed mines land reuse is extremely urgent. Exploration of the law of surface deformation in closed mine is therefore significance for the development of mining cities. In this study, we first used the StaMSBAS technique to reveal surface deformation of western Xuzhou coalfield from 16/6/2006 to 19/8/2018, based on 77 SAR images. The result of ALOS-1 monitoring was validated by levelling data, and it was shown that the root-mean-square-error and standard deviation were 31.8 mm and 28.9 mm, respectively. The law of surface deformation that occurred before and after mine closure was finally analysed. It was found that: 1) After mine closure, the surface deformation of Jiahe mine experienced the process of subsidence, relative stability and then uplift, and the surface deformations of Zhangxiaolou and Pangzhuang mines changed from slow subsidence to accelerated subsidence. 2) Where the maximum pre-closure subsidence occurred at Jiahe mine, the residual subsidence and uplift that occurred after mine closure was also significant, the correlation coefficients were 0.62 and 0.65, respectively. When the mines were active, surface subsidence of Jiahe and Zhangxiaolou mines was positively correlated with the strike mining degree, with correlation coefficients of 0.90 and 0.83, respectively. After Jiahe mine closure, the residual subsidence was positively correlated with the strike mining degree, and the surface uplift was positively correlated with the depth-to-thickness ratio, the correlation coefficients were both 0.70.

The offset-tracking method (OTM) utilizing SAR image intensity can detect large deformations, which makes up for the inability of interferometric synthetic aperture radar (InSAR) technology in large mining deformation monitoring, and has been widely used. Through lots of simulation experiments, it was found that the accuracy of OTM is associated with deformation gradients and image noises in the cross-correlation window (CCW), so CCW sizes should be selected reasonably according to deformation gradients and noise levels. Based on the above conclusions, this paper proposes an adaptive CCW selection method based on deformation gradients and image noises for mining deformation monitoring, and this method considers influences of deformation gradients and image noises on deformations to select adaptive CCWs. In consideration of noise influences on offset-tracking results, smaller CCWs are selected for large deformation gradient areas, and larger CCWs are selected for small deformation gradient areas. For some special areas, special CCWs are selected for offset-tracking. The proposed method is implemented to simulation and real experiments, and the experiment results demonstrate that the proposed method with high reliability and effectiveness can significantly improve the accuracy of OTM in mining deformation monitoring.

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.