scholarly journals Research on Surface Deformation of Ordos Coal Mining Area by Integrating Multitemporal D-InSAR and Offset Tracking Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jiaming Yao ◽  
Xin Yao ◽  
Zuoqi Wu ◽  
Xinghong Liu

Underground mining in coal mining areas will induce large-scale, large-gradient surface deformation, threatening the safety of people’s lives and property in nearby areas. Due to mining-related subsidence is characterized by fast displacement and high nonlinearity, monitoring this process by using traditional and single interferometric synthetic aperture radar (InSAR) technology is very challenging, and it cannot accurately and quantitatively calculate the deformation of the mining area. In this paper, we proposed a new method that combines both multitemporal consecutive D-InSAR and offset tracking technology to construct a complete deformation field of the coal mining area. Taking into account the accuracy of multitemporal consecutive D-InSAR in calculating small deformation areas and the ability of offset tracking to measure large deformation areas, we utilized their respective advantages to extract the surface influence range and applied an adaptive spatial filtering method to integrate their respective results for inversion of the deformation field. 12 ascending high-resolution TerraSAR-X images (2 m) from September 3, 2018, to October 26, 2019, and 39 descending Sentinel-1 TOPS SAR images from August 5, 2018, to November 4, 2019, in the Ordos Coalfield located at Inner Mongolia, China, were utilized to obtain the whole subsidence field of the working faces F6211 and F6207 during the 454-day mining period. The GPS monitoring station located in the direction of the mining surface is used to verify the accuracy of the above method; at the same time, to a certain extent, the difference between the unmanned aerial vehicle’s DSM data acquired after coal mining and the Shuttle Radar Topography Mission (STRM) DEM can qualitatively verify the accuracy of the results. Our results show that the results of TerraSAR are basically consistent with the deformation trend of GPS data, and that of Sentinel-1 have large errors compared with GPS. The maximum central subsidence reaches ~12 m in the working face F6211 and ~4 m in the working face F6207. In the working face F6207, the good agreement between GPS and TerraSAR results indicated that the method above using high-resolution SAR data could be reliable for monitoring the large deformation area in the mining field.

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiaqi Jin ◽  
Chicheng Yan ◽  
Yixuan Tang ◽  
Yilong Yin

Along with the accelerated shift of coal mining to the ecologically fragile west, the contradiction between coal resource development and ecological protection in the western arid and semiarid coal mining areas is rapidly intensifying. Based on the above background, this thesis takes the coal mining area in the arid and semiarid regions as an example; applies the theories of ecology, coal mining subsidence, geodesy, and ecological restoration; uses remote sensing in synthetic aperture radar (SAR), geographic information system (GIS), and mathematical modelling to reveal the ecological evolution law of the mining area; measures the ecological damage of the mining area; and then proposes a reasonable ecological restoration strategy. The surface deformation monitoring study in the study area shows that on the whole, some areas in the study area have different degrees of surface subsidence disasters, and the maximum surface subsidence value exceeds 800 mm. From the distribution of surface subsidence in the study area, surface subsidence disasters mainly occur in the eastern and central mountainous areas rich in coal resources, as well as in the mining areas west of the Yellow River, and the subsidence basins are distributed in a series of irregular concentric ovals. In terms of the scale of surface subsidence in the study area, a total of 230.03 km2 of land in the study area showed surface subsidence hazards during the monitoring period, accounting for 13.78% of the total area of the study area, of which the area of severe subsidence was 44.98 km2 (2.69%). The area of more serious subsidence area is 101.33 km2 (6.07%), and the area affected by subsidence is 83.72 km2 (5.01%).


2021 ◽  
Vol 2021 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiyong Wang ◽  
Jingzhao Zhang ◽  
Yaran Yu ◽  
Jian Liu ◽  
Wei Liu ◽  
...  

Excessive exploitation of underground mine resources has caused serious land subsidence in China. This paper focused on monitoring and modeling the single subsidence basin in coal mining area based on SAR interferometry (InSAR). The optimum InSAR processing strategy to monitor the mining subsidence was built to obtain the land subsidence with large deformation. And a method of three-dimensional mathematical modeling of single subsidence basin based on InSAR measurements was presented. Using Jining Coalfield (China) as the study area, we acquired 7 L-band PALSAR images from January 2008 to February 2010 to monitor the land subsidence in Jining Coalfield. The deformation maps in Jining Coalfield in different periods were obtained. Taking the Geting Coal Mine within the Jining coalfield as an example, we finely analyzed and interpreted the deformation maps. Compared with the simultaneous filed measurements, the precision of deformation measurement using D-InSAR in mining area was analyzed. The root mean square error was 1.37 cm. The method of fine interpretation and analysis for a single subsidence basin was established. The experiments have proved that InSAR technique with L-band InSAR data is suitable for monitoring mining subsidence with large deformation. And the 3D mathematical modeling method could be used for the single subsidence basin in coal mining area.


2020 ◽  
Vol 28 (2) ◽  
pp. 1409-1416
Author(s):  
Ana Paula Bigliardi ◽  
Caroline Lopes Feijo Fernandes ◽  
Edlaine Acosta Pinto ◽  
Marina dos Santos ◽  
Edariane Menestrino Garcia ◽  
...  

CATENA ◽  
2022 ◽  
Vol 209 ◽  
pp. 105830
Author(s):  
Dongdong Yang ◽  
Haijun Qiu ◽  
Shuyue Ma ◽  
Zijing Liu ◽  
Chi Du ◽  
...  

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