基于SBAS-InSAR技术的金沙江流域沃达村巨型老滑坡形变分析

冯文凯 顿佳伟 易小宇 张国强

冯文凯, 顿佳伟, 易小宇, 等. 2020. 基于SBAS-InSAR技术的金沙江流域沃达村巨型老滑坡形变分析[J]. 工程地质学报, 28(2): 384-393. doi: 10.13544/j.cnki.jeg.2019-411
引用本文: 冯文凯, 顿佳伟, 易小宇, 等. 2020. 基于SBAS-InSAR技术的金沙江流域沃达村巨型老滑坡形变分析[J]. 工程地质学报, 28(2): 384-393. doi: 10.13544/j.cnki.jeg.2019-411
Feng Wenkai, Dun Jiawei, Yi Xiaoyu, et al. 2020. Deformation analysis of Woda village old landslide in Jinsha river basin using SBAS-InSAR technology[J]. Journal of Engineering Geology, 28(2): 384-393. doi: 10.13544/j.cnki.jeg.2019-411
Citation: Feng Wenkai, Dun Jiawei, Yi Xiaoyu, et al. 2020. Deformation analysis of Woda village old landslide in Jinsha river basin using SBAS-InSAR technology[J]. Journal of Engineering Geology, 28(2): 384-393. doi: 10.13544/j.cnki.jeg.2019-411

基于SBAS-InSAR技术的金沙江流域沃达村巨型老滑坡形变分析

doi: 10.13544/j.cnki.jeg.2019-411
基金项目: 

国家自然科学基金 41977252

四川省青年科技创新研究团队专项计划项目 2017TD0018

地质灾害防治与地质环境保护国家重点实验室团队项目 SKLGP2016Z001

详细信息
    作者简介:

    冯文凯(1974-),男,博士,教授,博士生导师,主要从事区域稳定及与岩体稳定以及地质灾害评价与防治方面的教学与研究工作.E-mail:fengwenkai@cdut.cn

  • 中图分类号: P642.A

DEFORMATION ANALYSIS OF WODA VILLAGE OLD LANDSLIDE IN JINSHA RIVER BASIN USING SBAS-INSAR TECHNOLOGY

Funds: 

This study is supported by the National Natural Science Foundation of China 41977252

the Sichuan Provincial Youth Science and Technology Innovation Team Special Projects of China 2017TD0018

the Team Project of Independent Research of SKLGP SKLGP2016Z001

  • 摘要: 近年来突发性高位滑塌灾害日益频发,造成恶劣影响。这类地质灾害调查难度高、隐蔽性强,单靠群测群防和地质调查难以解决灾害的防治问题。随着雷达遥感卫星数据质量的不断提升,合成孔径干涉雷达测量(InSAR)中的SBAS-InSAR技术为特大型老滑坡灾前形变探测提供了新的技术途径。利用SBAS-InSAR技术对金沙江流域沃达村滑坡进行地表形变监测,获取了2017年3月30日至2019年9月28日内的形变结果,划定了强烈形变区(Ⅰ雷达)、均匀形变区(Ⅱ雷达),分析了滑坡复活区整体和局部滑塌地表形变速率、累积位移变化趋势和主裂缝形变情况。同时实地进行了工程地质调查和复核,发现老滑坡复活区变形迹象与SBAS-InSAR技术解译成果有着较好的一致性。表明SBAS-InSAR技术在复杂山区地质灾害监测预警领域有较为广阔的应用前景,为类似老滑坡监测预警提供了新的思路与借鉴。
  • 图  1  滑坡地理位置图

    Figure  1.  Geographic location map of the landslide

    图  2  综合光学遥感解译图

    Figure  2.  Integrated optical remote sensing interpretation map

    图  3  数据覆盖范围图

    Figure  3.  Data coverage chart

    图  4  影像时间基线

    Figure  4.  Image time baseline

    图  5  影像空间基线

    Figure  5.  Image spatial baseline

    图  6  SBAS处理流程

    Figure  6.  SBAS-InSAR processing flow

    图  7  雷达视线方向和坡度方向几何示意图(Cascini et al., 2010)

    Figure  7.  Geometric sketches of radar line- of -sight direction and slope direction(Cascini et al., 2010)

    图  8  2017年3月~2019年9月形变速率图(LOS方向)

    Figure  8.  Deformation rate map from March 2017 to September 2019(LOS Direction)

    图  9  2017年3月~2019年9月形变速率图(Slope方向)

    Figure  9.  Deformation rate map from March 2017 to September 2019(Slope Direction)

    图  10  2017年3月~2019年9月形变速率图(Vertical方向)

    Figure  10.  Deformation rate map from March 2017 to September 2019(Vertical Direction)

    图  11  沃达村老滑坡照片

    Figure  11.  Photographs of the old landslide in Woda Village

    图  12  InSAR时序形变与降雨响应

    Figure  12.  InSAR time series deformation and rainfall response

    图  13  2017年3~12月形变速率及剖面形变图

    Figure  13.  Deformation rate and profile deformation map from March to December 2017

    图  14  2018年形变速率及剖面形变图

    Figure  14.  Deformation rate and profile deformation map of 2018

    图  15  2019年1~9月形变速率及剖面形变图

    Figure  15.  Deformation rate and profile deformation map from January to September 2019

    图  16  H01变形体SBAS-InSAR监测与野外现象对比

    Figure  16.  Comparison of SBAS-InSAR monitoring data of H01 landslide with field review

    表  1  沃达村滑坡Sentinel 1A数据列表

    Table  1.   Data list of sentinel 1 in Dawo Village landslide

    序号 成像时间 成像
    模式
    极化
    方式
    累积时
    间基线
    /d
    累积空
    /m
    1 20170330 IW VV 0 0
    2 20170423 IW VV 24 -46.119
    3 20170517 IW VV 48 -50.186
    4 20170610 IW VV 72 77.826
    5 20170704 IW VV 96 40.558
    6 20170809 IW VV 132 -10.291
    7 20170902 IW VV 156 -39.573
    8 20170926 IW VV 180 -50.931
    9 20171020 IW VV 204 114.923
    10 20171113 IW VV 228 -24.411
    11 20171207 IW VV 252 50.346
    12 20171231 IW VV 276 63.293
    13 20180124 IW VV 300 -165.698
    14 20180217 IW VV 324 27.433
    15 20180313 IW VV 348 30.264
    16 20180406 IW VV 372 -6.096
    17 20180430 IW VV 396 23.623
    18 20180524 IW VV 420 -147.052
    19 20180617 IW VV 444 116.508
    20 20180711 IW VV 468 43.829
    21 20180723 IW VV 492 -86.624
    22 20180816 IW VV 516 -15.263
    23 20180909 IW VV 540 64.119
    24 20181003 IW VV 564 -43.180
    25 20181027 IW VV 588 35.953
    26 20181120 IW VV 612 26.433
    27 20181214 IW VV 636 -18.007
    28 20190107 IW VV 660 31.303
    29 20190131 IW VV 684 -57.897
    30 20190224 IW VV 708 -62.838
    31 20190320 IW VV 732 61.137
    32 20190413 IW VV 756 -40.041
    33 20190507 IW VV 780 81.387
    34 20190531 IW VV 804 -93.348
    35 20190624 IW VV 828 27.419
    36 20190718 IW VV 852 18.360
    37 20190811 IW VV 876 -105.069
    38 20190904 IW VV 900 36.953
    39 20190928 IW VV 924 41.097
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  • Berardino P, Fornaro G, Lanari R, et al. 2002. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J].IEEE Transactions on Geoscience & Remote Sensing, 40(11): 2375-2383. http://cn.bing.com/academic/profile?id=9267c9b76553eb4232501bf4d2f123df&encoded=0&v=paper_preview&mkt=zh-cn
    Cascini L, Fornaro G, Peduto D. 2010. Advanced low-and full-resolution D-InSAR map generation for slow-moving landslide analysis at different scales[J].Engineering Geology, 112(1): 29-42. https://www.sciencedirect.com/science/article/abs/pii/S0013795210000049
    Casu F, Manzo M, Lanari R. 2000. A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from D-InSAR data[J].Remote Sensing of Environment, 102(3): 195-210. https://www.sciencedirect.com/science/article/pii/S0034425706000526
    Colesanti C, Wasowski J. 2006. Investigating landslides with space-borne Synthetic Aperture Radar(SAR)interferometry[J].Engineering Geology, 88(3): 173-199. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=76e04e80aa3b1d5c4a0e9e7422064125
    Dai K R, Zhuo G C, Xu Q, et al. 2019. Tracing the pre-failure two-dimensional surface displacements of Nanyu Landslide, Gansu province with radar interferometry[J].Geomatics and Information Science of Wuhan University, 44(12): 1778-1786. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201912007
    Feng W K, Zhang G Q, Bai H L, et al. 2019. A preliminary analysis of the formation mechanism and development tendency of the huge Baige landslide in Jinshan River on October 11, 2018[J]. Journal of Engineering Geology, 27(2): 415-425.
    He X F, He M. 2012. InSAR earth observation data processing method and comprehensive measurement[M].Beijing: Science Press.
    Herrera G, Gutierrez F, Garcia-Davalillo J C, et al. 2013. Multi-sensor advanced DinSAR monitoring of very slow landslides: The Tena Valley case study(Central Spanish Pyrenees)[J]. Remote Sensing of Environment, 128(none): 31-43. http://cn.bing.com/academic/profile?id=e640af8be005cb07dd21f79ec3c05266&encoded=0&v=paper_preview&mkt=zh-cn
    Kang Y. 2016. Application of InSAR technology in landslide detection and monitoring in southwest mountainous areas[D].Xi'an: Chang'an University.
    Li L J, Yao X, Zhou Z K, et al. 2017. The deformation characteristics of a large landslide before and afire impoundment in the Xiluodu reservoir area based on InSAR technology[J].Journal of Engineering Geology, 25 (S1): 458-462.
    Liu X Y, Yang Z H, Guo C B, et al. 2017. Study of slow-moving landslide characteristics based on the SBAR-InSAR in the Xianshuihe fault zone[J].Geoscience, 31(5): 965-977. http://en.cnki.com.cn/Article_en/CJFDTotal-XDDZ201705008.htm
    Mo Y J, Wu Y, Liu X W. 2018. Monitoring the ground subsidence in Xiaojin County, Sichuan province based on small baseline subset technique[J].Engineering of Surveying and Mapping, 27(11): 46-50. http://d.old.wanfangdata.com.cn/Periodical/chgc201811009
    Nie B Q. 2018. Landslide deformation detection and identification based on InSAR technology——A case of Danba County[D]. Chengdu: Chengdu University of Technology.
    Tre altamira. 2017. Data in focus: precursor of Maoxian landslide measured from space[EB/OL].http://tre-altamira.com/news/data-focus-precursor-maoxian-landslide-measured-space/.2017-06-29.
    Wang J. 2018. Long-term spaceborne InSAR technology landslide geological disaster monitoring research[D].Beijing: Beijing Jiaotong University.
    Xu J Q, Ma T, Lu Y K, et al. 2019. Land subsidence monitoring in North Henan plain based on SBAS-InSAR technology[J].Journal of Jilin University(Earth Science Edition), 49(4): 1182-1191. http://d.old.wanfangdata.com.cn/Periodical/cckjdxxb201904025
    Xu Q, Li W L, Dong X J, et al. 2017. The Xinmocun landslide on June 24, 2017 in Maoxian, Sichuan: Characteristics and failure mechanism[J].Chinese Journal of Rock Mechanics and Engineering, 36(11): 2612-2628.
    Xu Q, Zheng G, Li W L, et al. 2018. Study on successive landslide damming events of Jinsha River in Baige Village on Octorber 11 and November 3, 2018[J]. Journal of Engineering Geology, 26(6): 1534-1551. http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201806017
    Yu R. 2014. Xi'an County landslide monitoring research based on short baseline(SBAS)technology[D].Nanjing: Nanjing Normal University.
    Zhang J, Feng D X, Qi W, et al. 2018. Monitoring land subsidence in Panjin region with SBAS-InSAR method[J].Journal of Engineering Geology, 26(4): 999-1007. http://d.old.wanfangdata.com.cn/Periodical/gcdzxb201804024
    Zhang Y. 2018. Surface deformation monitoring based on InSAR technology and early identification of landslide[D].Lanzhou: Lanzhou University.
    Zhao C Y, Zhang Q, Zhang J. 2011. Deformation monitoring of ground fissure with SAR inter-ferometry in Qingxu, Shanxi province[J].Journal of Engineering Geology, 19(1): 70-75. http://en.cnki.com.cn/Article_en/CJFDTOTAL-GCDZ201101014.htm
    戴可人, 卓冠晨, 许强, 等. 2019.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):44(12): 1778-1786. http://d.old.wanfangdata.com.cn/Periodical/whchkjdxxb201912007
    冯文凯, 张国强, 白慧林, 等. 2019.金沙江"10 ·11"白格特大型滑坡形成机制及发展趋势初步分析[J].工程地质学报, 27(2): 415-425. doi: 10.13544/j.cnki.jeg.2018-392
    何秀凤, 何敏. 2012. InSAR对地观测数据处理方法与综合测量[M].北京:科学出版社.
    康亚. 2016. InSAR技术在西南山区滑坡探测与监测的应用[D].西安: 长安大学.
    李凌婧, 姚鑫, 周振凯, 等. 2017.溪洛渡库区某大型滑坡蓄水前后变形特征InAR分析[J].工程地质学报, 25 (S1): 458-462. doi: 10.13544/j.cnki.jeg.2017.s1.071
    刘筱怡, 杨志华, 郭长宝, 等. 2017.基于SBAS-InSAR的鲜水河断裂带蠕滑型滑坡特征研究[J].现代地质, 31(5): 965-977. doi: 10.3969/j.issn.1000-8527.2017.05.008
    莫玉娟, 吴洋, 刘学武. 2018.基于SBAS技术的四川阿坝州小金县地表形变监测[J].测绘工程, 27(11): 46-50. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=chgc201811009
    聂兵其. 2018.基于InSAR的滑坡形变探测及隐患识别研究——以丹巴县城区为例[D].成都: 成都理工大学.
    许军强, 马涛, 卢意恺, 等. 2019.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), 49(4): 1182-1191. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cckjdxxb201904025
    许强, 李为乐, 董秀军, 等. 2017.四川茂县叠溪镇新磨村滑坡特征与成因机制初步研究[J].岩石力学与工程学报, 36(11): 2612-2628. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=yslxygcxb201711002
    许强, 郑光, 李为乐, 等. 2018.2018年10月和11月金沙江白格两次滑坡—堰塞堵江事件分析研究[J].工程地质学报, 26(6): 1534-1551. doi: 10.13544/j.cnki.jeg.2018-406
    余睿. 2014.基于短基线(SBAS)技术的西和县滑坡监测研究[D].南京: 南京师范大学.
    张静, 冯东向, 綦巍, 等. 2018.基于SBAS-InSAR技术的盘锦地区地面沉降监测[J].工程地质学报, 26(4): 999-1007. doi: 10.13544/j.cnki.jeg.2017-382
    张毅, 2018基于InSAR技术的地表变形监测与滑坡早期识别研究[D].兰州: 兰州大学.
    赵超英, 张勤, 张静. 2011.山西清徐地裂缝形变的InSAR监测分析[J].工程地质学报, 19(1): 70-75. doi: 10.3969/j.issn.1004-9665.2011.01.011
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出版历程
  • 收稿日期:  2019-10-08
  • 修回日期:  2019-12-27
  • 刊出日期:  2020-04-25

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