Deformation monitoring and analysis of mining areas based on the DT-SDFPT combined time-series InSAR

doi:10.6046/zrzyyg.2022378

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Abstract

High-intensity coal mining leads to significant surface deformation and secondary geological disasters. Synthetic aperture Radar interferometry (InSAR), exhibiting high deformation monitoring capability, fails to detect enough target pixels in the mining core and surrounding low-coherence areas. This study intends to increase the density and coverage of deformation monitoring points in mining areas by combining distributed targets (DTs) and slowly-decorrelating filtered phase targets (SDFPTs). First, DT and SDFPT candidate pixels were selected using the fast statistically homogenous pixel selection (FaSHPS) method and the amplitude dispersion index method, respectively for phase optimization and stability analysis. Then, qualified DT and SDFPT pixels were screened out to constitute a fused pixel set, which was subjected to three-dimensional phase unwrapping, phase time series recovery, and spatio-temporal filtering. Consequently, the deformation time series and the annual average deformation rate were determined based on the fused pixel set. Finally, the method proposed in this study was applied to monitor the deformation in the Buertai coal mine using 60 scenes of Sentinel-1 images covering the coal mine from April 2018 to April 2020. The results reveal a significant increase in the density and coverage of deformation points through the integration of DT and SDFPT, thus allowing for the monitoring of higher levels of maximum deformation. Within the experimental area, five deformation cones were identified, with the maximum cumulative deformation amplitude reaching -309.76 mm. The influencing range of the deformations and the difference in the deformation amplitude of the time series in different years are closely related to mining activities.

Keywords Buertai coal mine distributed target slowly-decorrelating filtered phase target surface deformation Sentinel-1A

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Issue Date: 13 March 2024

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	Bing YU
	Bing WANG
	Guoxiang LIU
	Guo ZHANG
	Yunliang HU
	Jinlong HU

Cite this article:

Bing YU,Bing WANG,Guoxiang LIU, et al. Deformation monitoring and analysis of mining areas based on the DT-SDFPT combined time-series InSAR[J]. Remote Sensing for Natural Resources, 2024, 36(1): 14-25.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022378 OR https://www.gtzyyg.com/EN/Y2024/V36/I1/14

Fig.1 Location of study area

Fig.2 Spatial-temporal baseline

Fig.3 Flow chart of data processing

Fig.4 Quality evaluation of data sets

Fig.5 Comparison between differential interferometric phases before and after optimization

Fig.6 Annual average deformation rate of LOS in the study area

Fig.7 Deformation accuracy verification by SBAS-InSAR

Fig.8 Deformation accuracy verification by PS-InSAR

Fig.9 Feature point distribution plot of the study area

Fig.10 Time series accumulative subsidence of the feature points

Fig.11 Time series deformation diagram of section line in Area A

Fig.12 Time series deformation diagram of section line in Area B

Fig.13 Time series deformation diagram of section line in Area C

[1]	许萍, 杨晶. 2018年中国能源产业回顾及2019年展望[J]. 石油科技论坛, 2019, 38(1):8-19.
[1]	Xu P, Yang J. Review of China’ energy industry in 2018 and outlook of 2019[J]. Oil Forum, 2019, 38(1):8-19.
[2]	何国清, 杨伦, 凌赓娣, 等. 矿山开采沉陷学[M]. 徐州: 中国矿业大学出版社,1991.
[2]	He G Q, Yang L, Liang G D, et al. Mining subsidence science[M]. Xuzhou: China University of Mining and Technology Press,1991.
[3]	Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1):8-20. doi: 10.1109/36.898661 url: http://ieeexplore.ieee.org/document/898661/
[4]	Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11):2375-2383. doi: 10.1109/TGRS.2002.803792 url: http://ieeexplore.ieee.org/document/1166596/
[5]	李德仁, 廖明生, 王艳. 永久散射体雷达干涉测量技术[J]. 武汉大学学报:信息科学版, 2004, 29(8):664-668.
[5]	Li D R, Liao M S, Wang Y. Progress of permanent scatterer interferometry[J]. Geomatics and Information Science of Wuhan University, 2004, 29(8):664-668.
[6]	李珊珊, 李志伟, 胡俊, 等. SBAS-InSAR技术监测青藏高原季节性冻土形变[J]. 地球物理学报, 2013, 56(5):1476-1486.
[6]	Li S S, Li Z W, Hu J, et al. Investigation of the seasonal oscillation of the permafrost over Qinghai-Tibet Plateau with SBAS-InSAR algorithm[J]. Chinese Journal of Geophysics, 2013, 56(5):1476-1486.
[7]	Chaussard E, Wdowinski S, Cabral-Cano E, et al. Land subsidence in central Mexico detected by ALOS InSAR time-series[J]. Remote Sensing of Environment, 2014, 140:94-106. doi: 10.1016/j.rse.2013.08.038 url: https://linkinghub.elsevier.com/retrieve/pii/S0034425713002964
[8]	Yu B, Liu G, Li Z, et al. Subsidence detection by TerraSAR-X interferometry on a network of natural persistent scatterers and artificial corner reflectors[J]. Computers & Geosciences, 2013, 58:126-136. doi: 10.1016/j.cageo.2013.04.020 url: https://linkinghub.elsevier.com/retrieve/pii/S0098300413001209
[9]	Ferretti A, Fumagalli A, Novali F, et al. A new algorithm for processing interferometric data-stacks:SqueeSAR[J]. IEEE Transactions on Geoscience & Remote Sensing, 2011, 49(9):3460-3470.
[10]	Dwivedi R, Narayan A B, Tiwari A, et al. Optimal estimation of interferometric phase for measuring surface deformation[J]. International Journal of Remote Sensing, 2017, 38(5):1339-1349. doi: 10.1080/01431161.2017.1280627 url: https://www.tandfonline.com/doi/full/10.1080/01431161.2017.1280627
[11]	范锐彦, 焦健, 高胜, 等. InSAR时序分析高相干目标选取方法比较研究[J]. 地球信息科学学报, 2016, 18(6):805-814. doi: 10.3724/SP.J.1047.2016.00805
[11]	Fan R Y, Jiao J, Gao S, et al. Comparison research of high coherent target selection based on InSAR time series analysis[J]. Journal of Geo-information Science, 2016, 18(6):805-814.
[12]	Parizzi A, Brcic R. Adaptive InSAR stack multilooking exploiting amplitude statistics:A comparison between different techniques and practical results[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(3):441-445. doi: 10.1109/LGRS.2010.2083631 url: http://ieeexplore.ieee.org/document/5634069/
[13]	李冠楠, 谭衢霖, 谢酬, 等. 基于DS-InSAR的复杂山区铁路沿线形变监测研究[J]. 中国铁路, 2019(2):59-65.
[13]	Li G N, Tan Q L, Xie C, et al. Monitoring study on deformation along complex mountain railway based on DS-InSAR[J]. China Railway, 2019(2):59-65.
[14]	Ao M, Zhang L, Dong Y, et al. Characterizing the evolution life cycle of the Sunkoshi landslide in Nepal with multi-source SAR data[J]. Scientific Reports, 2020, 10:17988. doi: 10.1038/s41598-020-75002-y pmid: 33093621
[15]	陈玉兴, 江利明, 梁林林, 等. 基于Sentinel-1 SAR数据的黑河上游冻土形变时序InSAR监测[J]. 地球物理学报, 2019, 62(7):2441-2454. doi: 10.6038/cjg2019M0255
[15]	Chen Y X, Jiang L M, Liang L L, et al. Monitoring permafrost deformation in the upstream Heihe River,Qilian Mountain by using multi-temporal Sentinel-1 InSAR dataset[J]. Chinese Journal of Geophysics, 2019, 62(7):2441-2454.
[16]	Goel K, Adam N. An advanced algorithm for deformation estimation in non-urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73:100-110. doi: 10.1016/j.isprsjprs.2012.06.001 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271612001013
[17]	蒋弥, 丁晓利, 何秀凤, 等. 基于快速分布式目标探测的时序雷达干涉测量方法:以Lost Hills油藏区为例[J]. 地球物理学报, 2016, 59(10):3592-3603. doi: 10.6038/cjg20161007
[17]	Jiang M, Ding X L, He X F, et al. FaSHPS-InSAR technique for distributed scatterers:A case study over the lost hills oil field,California[J]. Chinese Journal of Geophysics, 2016, 59(10):3592-3603.
[18]	李达, 邓喀中, 高晓雄, 等. 基于SBAS-InSAR的矿区地表沉降监测与分析[J]. 武汉大学学报(信息科学版), 2018, 43(10):1531-1537.
[18]	Li D, Deng K Z, Gao X X, et al. Monitoring and analysis of surface subsidence in mining area based on SBAS-InSAR[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10):1531-1537.
[19]	胡爽, 吴文豪, 龙四春, 等. 分布式目标在红庆河煤矿形变监测中的应用[J]. 大地测量与地球动力学, 2019, 39(12):1261-1264.
[19]	Hu S, Wu W H, Long S C, et al. Application of distributed scatterer in deformation monitoring of Hongqinghe coal mine[J]. Journal of Geodesy and Geodynamics, 2019, 39(12):1261-1264.
[20]	李毅, 蒋金雄, 杜玉玲, 等. 融合分布式目标的矿区采动地表时序InSAR监测[J]. 中国矿业大学学报, 2020, 49(6):1199-1206,1232.
[20]	Li Y, Jiang J X, Du Y L, et al. Surface subsidence monitoring by time series InSAR integrating with distributed targets in mining region[J]. Journal of China University of Mining & Technology, 2020, 49(6):1199-1206,1232.
[21]	李柱, 范洪冬, 高彦涛, 等. 基于DS-InSAR的乌达煤田火区长时序地表形变监测与分析[J]. 自然资源遥感, 2022, 34(3):138-145.doi:10.6046/zrzyyg.2021245.
[21]	Li Z, Fan H D, Gao Y T, et al. DS-InSAR-based monitoring and analysis of a long time series of surface deformation in the fire area of the Wuda Coal field[J]. Remote Sensing for Natural Resources, 2022, 34(3):138-145.doi:10.6046/zrzyyg.2021245.
[22]	Zhang Z, Wang C, Tang Y, et al. Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers[J]. International Journal of Applied Earth Observation and Geoinformation, 2015, 39:49-55. doi: 10.1016/j.jag.2015.02.007 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243415000392
[23]	蒋金雄, 杜玉玲, 陈宇, 等. 利用DS-InSAR技术监测沛北矿区地表形变[J]. 测绘通报, 2021(2):117-121. doi: 10.13474/j.cnki.11-2246.2021.0056
[23]	Jiang J X, Du Y L, Chen Y, et al. Monitoring and analysis of surface deformation in Peibei mining region based on DS-InSAR technique[J]. Bulletin of Surveying and Mapping, 2021(2):117-121. doi: 10.13474/j.cnki.11-2246.2021.0056
[24]	贾会会, 张海清, 李克达, 等. 融合分布式散射体时序InSAR技术在矿区形变调查中的应用[J]. 吉林大学学报(地球科学版), 2022, 52(1):202-213.
[24]	Jia H H, Zhang H Q, Li K D, et al. Application of fusion distributed scatterer time-series InSAR technique in deformation survey of mining area[J]. Journal of Jilin University(Earth Science Edition), 2022, 52(1):202-213.
[25]	朱建军, 李志伟, 胡俊. InSAR变形监测方法与研究进展[J]. 测绘学报, 2017, 46(10):1717-1733. doi: 10.11947/j.AGCS.2017.20170350
[25]	Zhu J J, Li Z W, Hu J. Research progress and methods of InSAR for deformation monitoring[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(10):1717-1733. doi: 10.11947/j.AGCS.2017.20170350
[26]	Hooper A. A multi‐temporal InSAR method incorporating both persistent scatterer and small baseline approaches[J]. Geophysical Research Letters, 2008, 35(16):L16302.
[27]	魏纪成, 张红霞, 白泽朝, 等. 融合D-InSAR与PS-InSAR的神东矿区开采沉陷监测方法[J]. 金属矿山, 2019(10):55-60.
[27]	Wei J C, Zhang H X, Bai Z C, et al. Integrated monitoring method of the mining subsidence of Shendong mining area based on D-InSAR and PS-InSAR technology[J]. Metal Mine, 2019(10):55-60.
[28]	Xu X, Ma C, Lian D, et al. Inversion and analysis of mining subsidence by integrating DInSAR,offset tracking,and PIM technology[J]. Journal of Sensors, 2020:4136837.
[29]	Ma C, Cheng X, Yang Y, et al. Investigation on mining subsidence based on multi-temporal InSAR and time-series analysis of the small baseline subset—Case study of working faces 22201-1/2 in Bu’ertai mine,Shendong coalfield,China[J]. Remote Sensing, 2016, 8(11):951. doi: 10.3390/rs8110951 url: http://www.mdpi.com/2072-4292/8/11/951
[30]	成晓倩, 马超, 康建荣, 等. 联合DInSAR和PIM技术的沉陷特征模拟和时序分析[J]. 中国矿业大学学报, 2018, 47(5):1141-1148.
[30]	Cheng X Q, Ma C, Kang J R, et al. Simulation and time series analysis of mining subsidence by integrating DInSAR and PIM technology[J]. Journal of China University of Mining & Technology, 2018, 47(5):1141-1148.
[31]	马威, 马超, 赵鹏飞, 等. 高分辨SAR相干系数图像的开采沉陷区提取[J]. 测绘科学, 2018, 47(5):115-120.
[31]	Ma W, Ma C, Zhao P F, et al. Extraction of mining subsidence area based on coherence image of high resolution SAR[J]. Science of Surveying and Mapping, 2018, 47(5):115-120.
[32]	周秀隆, 吴嘉林, 辛德林. 世界第一矿——布尔台煤矿设计综述[J]. 煤炭工程, 2011(3):4-7.
[32]	Zhou X L, Wu J L. Xin D L. Integrated innovation to design world No.One coal Mine-Buertai mine[J]. Coal Engineering, 2011(3):4-7.
[33]	Jiang M, Ding X, Tian X, et al. A hybrid method for optimization of the adaptive Goldstein filter[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 98:29-43. doi: 10.1016/j.isprsjprs.2014.09.012 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271614002378
[34]	蒋弥, 丁晓利, 李志伟. 时序InSAR同质样本选取算法研究[J]. 地球物理学报, 2018, 61(12):4767-4776. doi: 10.6038/cjg2018L0490
[34]	Jiang M, Ding X L, Li Z W. Homogeneous pixel selection algorithm for multitemporal InSAR[J]. Chinese Journal of Geophysics, 2018, 61(12):4767-4776.
[35]	Zhang Z, Zeng Q, Jiao J. Deformations monitoring in complicated-surface areas by adaptive distributed Scatterer InSAR combined with land cover:Taking the Jiaju landslide in Danba,China as an example[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 186:102-122. doi: 10.1016/j.isprsjprs.2022.02.004 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271622000387
[36]	Hooper A, Zebker H A. Phase unwrapping in three dimensions with application to InSAR time series[J]. Journal of the Optical Society of America, 2007, 24(9):2737-47.
[37]	Hooper A, Segall P, Zebker H. Persistent scatterer interferometric synthetic aperture radar for crustal deformation analysis,with application to Volcán Alcedo,Galápagos[J]. Journal of Geophysical Research:Solid Earth, 2007, 112(B7).

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