1. 三峡大学三峡库区地质灾害教育部重点实验室
2. 三峡大学防灾减灾湖北省重点实验室
3. 三峡大学土木与建筑学院
网络首发:2024-11-18,
纸质出版:2025
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刘艺梁, 樊西丰, 申高伟, 等. 基于时序InSAR技术的木鱼包滑坡时空变形特征分析[J]. 地质科技通报, 2025,44(2):78-93.
刘艺梁, 樊西丰, 申高伟, et al. 基于时序InSAR技术的木鱼包滑坡时空变形特征分析[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 78-93.
刘艺梁, 樊西丰, 申高伟, 等. 基于时序InSAR技术的木鱼包滑坡时空变形特征分析[J]. 地质科技通报, 2025,44(2):78-93. DOI: 10.19509/j.cnki.dzkq.tb20240489.
刘艺梁, 樊西丰, 申高伟, et al. 基于时序InSAR技术的木鱼包滑坡时空变形特征分析[J]. Bulletin of Geological Science and Technology, 2025, 44(2): 78-93. DOI: 10.19509/j.cnki.dzkq.tb20240489.
自2003年三峡库区蓄水以来,木鱼包滑坡持续变形,严重威胁长江航运和库区人民生命财产安全。为全面精准地分析滑坡地表变形信息,采用斯坦福永久散射体-多时相InSAR(StaMPS-MTI)技术和短基线集时序InSAR(SBAS-InSAR)技术结合哨兵一号数据反演木鱼包滑坡2017-2022年的地表形变,并与全球导航卫星系统(GNSS)监测数据进行对比分析,结合2种技术的优点,点面结合对滑坡的时空变形特征进行了分区研究。研究结果表明:InSAR技术得到的形变信息可靠,2种时序InSAR技术各有优劣;对比各个分区的形变速率区间值,滑坡东侧坡面(-30.6~-46.2 mm/a)>主滑面东侧(-25.2~-37.8 mm/a)>主滑面西侧(-21.5~-31.5 mm/a)。在InSAR形变结果和前人研究基础上,对木鱼包滑坡变形模式进行了总结:木鱼包滑坡变形受降雨和库水影响,分为整体变形和局部变形2种。在高水位运行期,滑坡受浮托减重作用发生整体变形,库水阈值约为168 m。强降雨入渗岩体使地下水位升高,促使整体变形的同时,影响浅层土体和破碎岩体导致局部变形。在库水位下降期,滑坡受浮托减重和动水压力共同影响,其中浮托减重作用占主导地位,动水压力存在约36 d的滞后时间。在低水位运行期和库水位上升期,整体变形停止,强降雨使局部变形区发生变形。研究结果证明时序InSAR技术可以有效识别和监测滑坡,可以为地质灾害防治、风险评价提供技术支撑。
Since the impoundment of the Three Gorges Reservoir Area in 2003
the Muyubao landslide has continuously deformed
posing significant risks to Yangtze River navigation and the safety of people's lives and property in the reservoir area. ObjectiveTo more comprehensively and accurately analyze the surface deformation information of the landslide
Methodsthis study employs the Stanford method for persistent scatterers-multi-temporal InSAR (StaMPS-MTI) and small baseline subset InSAR (SBAS-InSAR) technology
combined with Sentinel-1 data
to invert the deformation information of the Muyubao landslide from 2017 to 2022. The deformation information is compared with GNSS monitoring data
and a regional analysis of the spatio-temporal deformation characteristics of the landslide is conducted by integrating the advantages of both technologies through a combination of point and surface measurements. ResultsThe results confirm that the deformation information obtained by InSAR technology is reliable
and each time-series InSAR technology has its own strengths and limitations. Specifically
the deformation rate intervals are as follows: the eastern slope of the landslide (−30.6 to −46.2 mm/year) > the eastern side of the major slipping plane (−25.2 to −37.8 mm/year) > the western side of the major slipping plane (−21.5 to −31.5 mm/year). ConclusionBased on the InSAR deformation results and previous studies
the deformation mode of the Muyubao landslide can be summarized as follows: the landslide undergoes overall and local deformation influenced by rainfall and reservoir water levels. During high water level periods
buoyancy-induced weight loss causes overall deformation
with a critical water level threshold of approximately 168 m. Heavy rainfall infiltrates the rock mass
raising the groundwater level
which promotes overall deformation and triggers local deformation in shallow soil and fractured rock masses. During the reservoir water decline period
the landslide is influenced by both buoyancy-induced weight loss and hydrodynamic pressure
with buoyancy effects being dominant; the hydrodynamic pressure effect lags by about 36 days. During low water level and rising water periods
overall deformation ceases
and heavy rainfall primarily causes localized deformation. The results indicate that time-series InSAR technology can effectively identify and monitor landslides
providing technical support for geological disaster prevention and risk assessment.
张毅.基于InSAR技术的地表变形监测与滑坡早期识别研究[D].兰州大学,2018(11).
徐潇宇.三峡库区地质灾害防治系统运行机制研究[D].中国地质大学,2013(01).
魏瑞琦.三峡库区木鱼包滑坡变形机理分析及稳定性预测评价[D].三峡大学,2022(03).
张富灵.库水及降雨作用下滑移-弯曲型滑坡形成条件与演化机制研究[D].三峡大学,2021(03).
Jinlong Zhang,Rui Yang,Yuan Qi,Hui Zhang,Juan Zhang,Qianhong Guo,Chao Ma,Hongwei Wang.A study on the monitoring of landslide deformation disasters in Wenxian County, Longnan City based on different time-series InSAR techniques[J].Natural Hazards,2024.
Zhang Tiantian,Li Bin,Gao Yang,Gao Haoyuan,Yin Yueping.Massive glacier-related geohazard chains and dynamics analysis at the Yarlung Zangbo River downstream of southeastern Tibetan Plateau[J].Bulletin of Engineering Geology and the Environment,2023.
Zhang Xingchen,Chen Lixia,Zhou Chao.Deformation Monitoring and Trend Analysis of Reservoir Bank Landslides by Combining Time-Series InSAR and Hurst Index[J].Remote Sensing,2023.
Yao Jiaming,Yao Xin,Liu Xinghong.Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR: A Case Study in Gongjue County, Tibet, China[J].Remote Sensing,2022(19).
Huang Faming,Chen Jiawu,Liu Weiping,Huang Jinsong,Hong Haoyuan,Chen Wei.Regional rainfall-induced landslide hazard warning based on landslide susceptibility mapping and a critical rainfall threshold[J].Geomorphology,2022.
Tsironi Varvara,Ganas Athanassios,Karamitros Ioannis,Efstathiou Eirini,Koukouvelas Ioannis,Sokos Efthimios.Kinematics of Active Landslides in Achaia (Peloponnese, Greece) through InSAR Time Series Analysis and Relation to Rainfall Patterns[J].Remote Sensing,2022.
Andrew Hooper,David Bekaert,Karsten Spaans,Mahmut Ar?kan.Recent advances in SAR interferometry time series analysis for measuring crustal deformation[J].Tectonophysics,2011.
Andrew Hooper,Howard Zebker,Paul Segall,Bert Kampes.A new method for measuring deformation on volcanoes and other natural terrains using InSAR persistent scatterers[J].Geophysical Research Letters,2004.
Berardino P.,Fornaro G.,Lanari R.,Sansosti E..A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J].IEEE Transactions on Geoscience and Remote Sensing,2002.
Alessandro Ferretti,Claudio Prati,Fabio Rocca.Permanent scatterers in SAR interferometry.[J].IEEE Trans. Geoscience and Remote Sensing,2001.
Stefania Usai,Roland Klees.SAR interferometry on a very long time scale: a study of the interferometric characteristics of man-made features.[J].IEEE Trans. Geoscience and Remote Sensing,1999.
Andrew K. Gabriel,Richard M. Goldstein,Howard A. Zebker.Mapping small elevation changes over large areas: Differential radar interferometry[J].Journal of Geophysical Research: Solid Earth,1989.
包馨,张瑞,刘安梦云,王婷,向卫,刘国祥.联合升降轨时序InSAR的金沙江滑坡群隐患识别[J].北京理工大学学报,2023(11).
刘涛,张明,王立朝,杨龙,殷保国.甘肃舟曲江顶崖古滑坡形成演化机理与堆积体稳定性评价[J].地质科技通报,2024(03).
赵玉,陈丽霞,梁梦姣.基于LSTM_TCN模型的降雨型滑坡时间概率预测及气象预警建模[J].地质科技通报,2024(02).
杨沛璋,崔圣华,裴向军,何双,王慧,梁玉飞.基于SBAS-InSAR和光学遥感影像的大型倾倒变形体变形演化[J].地质科技通报,2023(06).
朱智富,甘淑,张荐铭,袁希平,王睿博,张晓伦.结合SBAS-InSAR技术及信息熵的苍山地质滑坡隐患识别[J].测绘通报,2022(11).
朱鸿鹄,王佳,李厚芝,叶霄,施斌,张勤.基于数据挖掘的三峡库区特大滑坡变形关联规则研究[J].工程地质学报,2022(05).
王佳,朱鸿鹄,叶霄,李厚芝,张巍,程刚.考虑时滞效应的库区滑坡位移预测——以新铺滑坡为例[J].工程地质学报,2022(05).
唐辉明.重大滑坡预测预报研究进展与展望[J].地质科技通报,2022(06).
张凯翔,张占荣,于宪煜.SBAS-InSAR和PS-InSAR技术在鲁西南某线性工程沿线地面沉降成因分析中的应用[J].中国地质灾害与防治学报,2022(04).
李振洪,朱武,余琛,张勤,张成龙,刘振江,张雪松,陈博,杜建涛,宋闯,韩炳权,周佳薇.雷达影像地表形变干涉测量的机遇、挑战与展望[J].测绘学报,2022(07).
赵蓓蓓,黄海峰,邓永煌,董志鸿,柳青,薛蓉花.基于Sentinel-1A的三峡库区范家坪滑坡InSAR监测分析[J].人民长江,2022(10).
朱同同,史绪国,周超,蒋厚军,张路,廖明生.利用2016—2020年Sentinel-1数据监测与分析三峡库区树坪滑坡稳定性[J].武汉大学学报(信息科学版),2021(10).
冯文凯,易小宇,孟睿,肖锐铧,苏永超,朱权威.三峡库区木鱼包滑坡不同库水升降速率变形响应离心模型试验研究[J].水利学报,2021(05).
张亚迪,李煜东,董杰,范强,车彬,张路,周杨,廖明生.时序InSAR技术探测芒康地区滑坡灾害隐患[J].遥感学报,2019(05).
赵超英,刘晓杰,张勤,彭建兵,许强.甘肃黑方台黄土滑坡InSAR识别、监测与失稳模式研究[J].武汉大学学报(信息科学版),2019(07).
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(07).
郑坤,聂运菊,罗跃,李永飞.StaMPS-MTI技术在上海市地面沉降监测中的应用[J].江西科学,2019(01).
邓茂林,易庆林,韩蓓,周剑,李卓骏,张富灵.长江三峡库区木鱼包滑坡地表变形规律分析[J].岩土力学,2019(08).
范景辉,邱阔天,夏耶,李曼,林昊,张洪涛,涂鹏飞,刘广,舒思齐.三峡库区范家坪滑坡地表形变InSAR监测与综合分析[J].地质通报,2017(09).
姜兆英,于胜文,陶秋香.StaMPS-MTI技术在地面沉降监测中的应用[J].西南交通大学学报,2017(02).
唐亚明,张茂省,薛强,毕俊擘.滑坡监测预警国内外研究现状及评述[J].地质论评,2012(03).
黄润秋,向喜琼,巨能攀.我国区域地质灾害评价的现状及问题[J].地质通报,2004(11).
殷坤龙,张桂荣.地质灾害风险区划与综合防治对策[J].安全与环境工程,2003(01).
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