PU Chuanhao, XU Qiang, JIANG Ya'nan, ZHAO Kuanyao, HE Pan, ZHANG Hanyue, LI Huajin. Analysis of Land Subsidence Distribution and Influencing Factors in Yan'an New District Based on Time Series InSAR[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1728-1738. DOI: 10.13203/j.whugis20190372
Citation: PU Chuanhao, XU Qiang, JIANG Ya'nan, ZHAO Kuanyao, HE Pan, ZHANG Hanyue, LI Huajin. Analysis of Land Subsidence Distribution and Influencing Factors in Yan'an New District Based on Time Series InSAR[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1728-1738. DOI: 10.13203/j.whugis20190372

Analysis of Land Subsidence Distribution and Influencing Factors in Yan'an New District Based on Time Series InSAR

Funds: 

The National Natural Science Foundation of China 41790445

The National Natural Science Foundation of China 41630640

More Information
  • Author Bio:

    PU Chuanhao, PhD candidate, specializes in geological disaster evaluation and prediction. E-mail: 2529456063@qq.com

  • Corresponding author:

    XU Qiang, PhD, professor. E-mail: xq@cdut.edu.cn

  • Received Date: October 09, 2019
  • Published Date: November 18, 2020
  •   Objectives  The "mountain excavation and city construction" in Yan'an New District is the largest geotechnical project in the collapsible loess gully region. Large-scale land creation, engineering construction and complex engineering geological conditions has caused a large number of uneven land subsidence, which is posing an increasing threat to the stability of urban infrastructure and public safety.
      Methods  The ascending Sentinel-1A data stacks obtained during December 2017 to December 2018 were analyzed using the time series interferometric synthetic aperture radar (InSAR) technique, and the distribution characteristics of the post-construction land subsidence of the mountain excavation and city construction in Yan'an New District were obtained. The reliability of the detection results of InSAR technology was demonstrated by field investigation. Finally, based on the deformation monitoring results, the main influencing factors of land subsidence distribution were analyzed.
      Results  (1) The results show that significant uneven land subsidence formed after the mountain excavation and city construction in Yan'an New District is mainly distributed in the filling area, and the maximum deformation rate reaches up to 120.1 mm/a. Field investigation validates the reliability and accuracy of the deformation results. (2)The remodeling of original loess and the change of its physical properties during the filling process is the main intrinsic factors of land subsidence in the filling area. Large-scale land creation determines the distribution of land subsidence, and the thickness of the filling are the main controlling factor of the distribution and size of land subsidence. In addition, human activities and changes in the geo-environment will also accelerate the development of land subsidence.
      Conclusions  In order to achieve the early detection of potential geo-hazards and active risk prevention in the site selection and feasibility study stage of mega engineering projects in the Loess Plateau, the time-series InSAR technology can provide an effective technical that allows for the early detection of potential geo-hazards such as land subsidence and landslides, and provide a scientific basis for further monitoring and warning, planning and construction, geologic disaster prevention.
  • [1]
    Juang C, Dijkstra T, Wasowski J, et al. Loess Geohazards Research in China:Advances and Challenges for Mega Engineering Projects[J].Engineering Geology, 2019, 251:1-10 doi: 10.1016/j.enggeo.2019.01.019
    [2]
    崔亚军.延安新区建设的资源环境效益研究[D].西安: 长安大学, 2015

    Cui Yajun. Study on the Benefit of Resources and Environment in Yan'an New Area[D]. Xi'an: Chang'an University, 2015
    [3]
    Li P Y, Qian H, Wu J H. Accelerate Research on Land Creation[J]. Nature, 2014, 510(7 503):29-31 doi: 10.1038/510029a
    [4]
    廖明生, 王腾.时间序列InSAR技术与应用[M].北京:科学出版社, 2014

    Liao Mingsheng, Wang Teng.Time Series InSAR Technology and Its Applications[M].Beijing:Science Press, 2014
    [5]
    史绪国, 张路, 许强, 等.黄土台塬滑坡变形的时序InSAR监测分析[J].武汉大学学报·信息科学版, 2019, 44(7):1 027-1 034 doi: 10.13203/j.whugis20190056

    Shi Xuguo, Zhang Lu, Xu Qiang, et al. Monitoring Slope Displacements of Loess Terrace Using Time Series InSAR Analysis Technique[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):1 027-1 034 doi: 10.13203/j.whugis20190056
    [6]
    许军强, 马涛, 卢意恺, 等.基于SBAS-InSAR技术的豫北平原地面沉降监测[J].吉林大学学报(地球科学版), 2019, 49 (4):1 182-1 191 http://www.cnki.com.cn/Article/CJFDTotal-CCDZ201904025.htm

    Xu Junqiang, Ma Tao, Lu Yikai, et al.Land Subsidence Monitoring in North Henan Plain Based on SBAS-InSAR Technology[J]. Journal of Jilin University(Earth Science Edition), 2019, 49 (4):1 182-1 191 http://www.cnki.com.cn/Article/CJFDTotal-CCDZ201904025.htm
    [7]
    张路, 廖明生, 董杰, 等.基于时间序列InSAR分析的西部山区滑坡灾害隐患早期识别:以四川丹巴为例[J].武汉大学学报·信息科学版, 2018, 43(12):2 039-2 049 doi: 10.13203/j.whugis20180181

    Zhang Lu, Liao Mingsheng, Dong Jie, et al. Early Detection of Landslide Hazards in Mountainous Areas of West China Using Time Series SAR Interferometry:A Case Study of Danba, Sichuan[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):2 039-2 049 doi: 10.13203/j.whugis20180181
    [8]
    陆会燕, 李为乐, 许强, 等.光学遥感与InSAR结合的金沙江白格滑坡上下游滑坡隐患早期识别[J].武汉大学学报·信息科学版, 2019, 44(9):1 342-1 354 doi: 10.13203/j.whugis20190086

    Lu Huiyan, Li Weile, Xu Qiang, et al. Early Detection of Landslides in the Upstream and Downstream Areas of the Baige Landslide, the Jinsha River Based on Optical Remote Sensing and InSAR Technologies[J]. Geomatics and Information Science of Wuhan University, 2019, 44(9):1 342-1 354 doi: 10.13203/j.whugis20190086
    [9]
    许强, 董秀军, 李为乐.基于天-空-地一体化的重大地质灾害隐患早期识别与监测预警[J].武汉大学学报·信息科学版, 2019, 44(7):957-966 doi: 10.13203/j.whugis20190088

    Xu Qiang, Dong Xiujun, Li Weile. Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7):957-966 doi: 10.13203/j.whugis20190088
    [10]
    葛大庆, 戴可人, 郭兆成, 等.重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J].武汉大学学报·信息科学版, 2019, 44(7): 949-956 doi: 10.13203/j.whugis20190094

    Ge Daqing, Dai Keren, Guo Zhaocheng, et al. Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 949-956 doi: 10.13203/j.whugis20190094
    [11]
    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): 2 375-2 383 doi: 10.1109/TGRS.2002.803792
    [12]
    Lanari R, Mora O, Manunta M, et al. A Small-Baseline Approach for Investigating Deformations on Full-Resolution Differential SAR Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(7): 1 377-1 386 doi: 10.1109/TGRS.2004.828196
    [13]
    Lanari R, Casu F, Manzo M, et al. An Overview of the Small Baseline Subset Algorithm: A DInSAR Technique for Surface Deformation Analysis[J]. Pure and Applied Geophysics, 2007, 164(4):637-661 doi: 10.1007/s00024-007-0192-9
    [14]
    Mora O, Mallorqui J J, Broquetas A. Linear and Nonlinear Terrain Deformation Maps from a Reduced Set of Interferometric SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10):2 243-2 253 doi: 10.1109/TGRS.2003.814657
    [15]
    杨成生, 张勤, 赵超英, 等.短基线集InSAR技术用于大同盆地地面沉降、地裂缝及断裂活动监测[J].武汉大学学报·信息科学版, 2014, 39(8): 945-950 doi: 10.13203/j.whugis20130656

    Yang Chengsheng, Zhang Qin, Zhao Chaoying, et al. Small Baseline Subset InSAR Technology Used in Datong Basin Ground Subsidence, Fissure and Fault Zone Monitoring[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8): 945-950 doi: 10.13203/j.whugis20130656
    [16]
    董少春, 种亚辉, 胡欢, 等.基于时序InSAR的常州市2015—2018年地面沉降监测[J].南京大学学报(自然科学), 2019, 55(3):370-380 http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&filename=NJDZ201903004

    Dong Shaochun, Chong Yahui, Hu Huan, et al. Ground Subsidence Monitoring During 2015—2018 in Changzhou Based on Time Series InSAR Method[J].Journal of Nanjing University(Natural Science), 2019, 55(3):370-380 http://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFD&filename=NJDZ201903004
    [17]
    Peng M M, Zhao C Y, Zhang Q, et al. Research on Spatiotemporal Land Deformation (2012—2018) over Xi'an, China, with Multi-sensor SAR Datasets[J]. Remote Sensing, 2019, 11(6):664 doi: 10.3390/rs11060664
    [18]
    葛苗苗, 李宁, 张炜, 等.黄土高填方沉降规律分析及工后沉降反演预测[J].岩石力学与工程学报, 2017, 36(3):745-753 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201703023

    Ge Miaomiao, Li Ning, Zhang Wei, et al. Settlement Behavior and Inverse Prediction of Post-Construction Settlement of High Filled Loess Embankment[J].Chinese Journal of Rock Mechanics and Engineering, 2017, 36(3):745-753 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201703023
    [19]
    杜伟飞, 郑建国, 刘争宏, 等.黄土高填方地基沉降规律及排气条件影响[J].岩土力学, 2019, 40(1):325-331 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytlx201901033

    Du Weifei, Zheng Jianguo, Liu Zhenghong, et al. Settlement Behavior of High Loess-Filled Foundation and Impact from Exhaust Conditions[J]. Rock and Soil Mechanics, 2019, 40(1):325-331 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytlx201901033
    [20]
    郑建国, 曹杰, 张继文, 等.基于离心模型实验的黄土高填方沉降影响因素分析[J].岩石力学与工程学报, 2019, 38(3):560-571 http://www.cnki.com.cn/Article/CJFDTotal-YSLX201903012.htm

    Zheng Jianguo, Cao Jie, Zhang Jiwen, et al. Analysis of Influencing Factors of High Loess-Filled Foundations Based on Centrifugal Model Tests[J]. Chinese Journal of Rock Mechanics and Engineering, 2019, 38(3):560-571 http://www.cnki.com.cn/Article/CJFDTotal-YSLX201903012.htm
    [21]
    孔洋, 阮怀宁, 黄雪峰.延安地区压实马兰黄土高压固结变形特性[J].岩土力学, 2018, 39(5):1 731-1 736 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytlx201805023

    Kong Yang, Ruan Huaining, Huang Xuefeng. Deformation Characteristics of Compacted Malan Loess in Yan'an Region Under High Consolidation Pressure[J]. Rock and Soil Mechanics, 2018, 39(5):1 731-1 736 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytlx201805023
    [22]
    霍晨琛.地下水位上升对黄土高填方边坡稳定性的影响研究[D].西安: 长安大学, 2016 http://cdmd.cnki.com.cn/Article/CDMD-10710-1016749135.htm

    Huo Chenchen. Study on the Stability of High-filled Loess Slope Under the Rise of Groundwater Level[D]. Xi'an: Chang'an University, 2016 http://cdmd.cnki.com.cn/Article/CDMD-10710-1016749135.htm
    [23]
    刘颖莹.延安新区高填方黄土湿陷变形实验及其数值模拟研究[D].西安: 西北大学, 2018

    Liu Yingying. Experimental Study and Numerical Simulation on the Collapsibility of High Embankment Loess in Yan'an New District[D]. Xi'an: Northwest University, 2018
    [24]
    张继文, 于永堂, 李攀, 等.黄土削峁填沟高填方地下水监测与分析[J].西安建筑科技大学学报(自然科学版), 2016, 48(4): 477-483 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajzkjdx201604003

    Zhang Jiwen, Yu Yongtang, Li Pan, et al. Groundwater Monitoring and Analysis of High Fill Foundation in Loess Hilly-Gully Region[J].Journal of Xi'an University of Architecture and Technology (Natural Science Edition), 2016, 48(4): 477-483 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=xajzkjdx201604003
    [25]
    戴可人, 卓冠晨, 许强, 等.雷达干涉测量对甘肃南峪乡滑坡灾前二维形变追溯[J].武汉大学学报·信息科学版, 2019, 44(12):1 778-1 786 doi: 10.13203/j.whugis20190092

    Dai Keren, Zhuo Guanchen, Xu Qiang, et al. Tracing the Pre-failure Two-Dimension Surface Displacements of Nanyu Landslide, Gansu Province with Radar Interferometry[J]. Geomatics and Information Science of Wuhan University, 2019, 44(12):1 778-1 786 doi: 10.13203/j.whugis20190092
    [26]
    蒲川豪, 许强, 赵宽耀, 等.基于遥感分析的延安新区平山造城工程地面沉降及植被恢复特征研究[J].工程地质学报, 2020, 28(3):597-609 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb202003017

    Pu Chuanhao, Xu Qiang, Zhao Kuanyao, et al. Study on Land Subsidence and Vegetation Restoration Characteristics in Yan'an Mountain Excavation and City Construction Project Area Based on Remote Sensing Analysis[J]. Journal of Engineering Geology, 2020, 28(3):597-609 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb202003017
    [27]
    张静, 冯东向, 綦巍, 等.基于SBAS-InSAR技术的盘锦地区地面沉降监测[J].工程地质学报, 2018, 26(4) : 999-1 007 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201804024

    Zhang Jing, Feng Dongxiang, Qi Wei, et al. Monitoring Land Subsidence in Panjin Region with SBAS-InSAR Method[J]. Journal of Engineering Geology, 2018, 26(4) : 999-1 007 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=gcdzxb201804024
    [28]
    Wu Q, Jia C, Chen S, et al. SBAS-InSAR Based Deformation Detection of Urban Land, Created from Mega-Scale Mountain Excavating and Valley Filling in the Loess Plateau: The Case Study of Yan'an City[J]. Remote Sensing, 2019, 11(14):1 673-1 692 doi: 10.3390/rs11141673
    [29]
    陈开圣.公路工程压实黄土的强度与变形及其微观结构研究[D].西安: 长安大学, 2006 http://cdmd.cnki.com.cn/article/cdmd-11941-2006163423.htm

    Chen Kaisheng. Study on Strength and Deformation of Compacted Loess and Its Microstructure for Highway Engineering[D]. Xi'an: Chang'an University, 2006 http://cdmd.cnki.com.cn/article/cdmd-11941-2006163423.htm
    [30]
    伍石生, 武建民, 戴经梁.压实黄土湿陷变形问题的研究[J].西安公路交通大学学报, 1997, 17(3):1-3 http://www.cnki.com.cn/Article/CJFDTotal-XAGL703.000.htm

    Wu Shisheng, Wu Jianmin, Dai Jingliang. Study on Wetting-Collapse of Compacted Loess[J]. Journal of Xi'an Highway University, 1997, 17(3):1-3 http://www.cnki.com.cn/Article/CJFDTotal-XAGL703.000.htm
    [31]
    Chen G, Zhang Y, Zeng R Q, et al. Detection of Land Subsidence Associated with Land Creation and Rapid Urbanization in the Chinese Loess Plateau Using Time Series InSAR: A Case Study of Lanzhou New District[J]. Remote Sensing, 2018, 10(2):270-292 doi: 10.3390/rs10020270
    [32]
    张沛然.沟谷填方非饱和黄土填料增减湿变形特性、模型及结构性分析研究[D].兰州: 兰州理工大学, 2018

    Zhang Peiran. Study on the Moistening and Drying Deformation Characteristics, Model and Structural Analysis of the Unsaturated Loess Filling with Gully Fill[D]. Lanzhou: Lanzhou University of Technology, 2018
    [33]
    段旭, 董琪, 门玉明, 等.黄土沟壑高填方工后地下水与土体含水率变化研究[J].岩土工程学报, 2018, 40(9):1 753-1 758 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytgcxb201809025

    Duan Xu, Dong Qi, Men Yuming, et al. Change of Groundwater and Water Content of Loess High Fill in Gully Regions[J]. Chinese Journal of Geotechnical Engineering, 2018, 40(9):1 753-1 758 http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ytgcxb201809025
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