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遥感技术与应用  2023, Vol. 38 Issue (5): 1054-1061    DOI: 10.11873/j.issn.1004-0323.2023.5.1054
InSAR专栏     
基于短基线DInSAR的白鹤滩库区蓄水期滑坡隐患广域快速动态识别
吴明堂1(),房云峰1,沈月2,3,戴可人2,3(),姚义振1,陈建强1,冯文凯2
1.浙江华东岩土勘察设计研究院有限公司,浙江 杭州 310030
2.成都理工大学 地质灾害防治与地质环境保护国家重点实验室,四川 成都 610059
3.成都理工大学 地球科学学院,四川 成都 610059
Fast Dynamic Identification of Landslide Hazards in Baihetan Reservoir area based on Short Baseline DInSAR Interferometry
Mingtang WU1(),Yunfeng FANG1,Yue SHEN2,3,Keren DAI2,3(),Yizhen YAO1,Jianqiang CHEN1,Wenkai FENG2
1.Zhejiang Huadong Geotechnical Investigation & Design Institute Company Limited,Hangzhou,310030
2.State Key Laboratory of Geohazard Prevention and Geoenvironment Protection,Chengdu University of Technology,Chengdu 610059,China
3.School of Earth Science,Chengdu University of Technology,Chengdu 610059,China
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摘要:

白鹤滩水电是国内继三峡水电站之后的第二大水电站,于2021年4月开始蓄水,水位上升约150 m,库区运行期随着水位线的快速变化,改变了库区的地质环境,极易引发突发性的地质灾害。为了保障水电站的正常运行及库区的居民生命财产安全,需要对库区进行快速动态的地质灾害识别。因此,基于短基线DInSAR方法对金沙江流域白鹤滩库区重点库岸段(葫芦口—象鼻岭地段)蓄水期展开了地质灾害隐患识别。通过Sentinel-1A/B数据进行联合监测,将重返周期提高到6 d 1景,获取了研究区蓄水期2021年4月至11月共66景数据,结合SRTM DEM数据进行基于同一主影像的DInSAR处理,再将干涉对进行组合分析以达到快速动态的形变监测。最终共识别出92处存在显著形变的强形变区,均分布在金沙江两岸,其中8个强形变区域已被目前最高水位线800 m淹没,38个强形变区域位于水位线附近,部分出现小规模塌岸。同时选取了4个持续形变的区域结合实际地形地貌进行分析,发现形变迹象较为明显,且强形变区域位置与DInSAR方法监测结果相吻合,证明了将DInSAR方法应用于快速动态的发现新隐患点的情况的可行性,进行了蓄水期灾害分析与监测预警对保障白鹤滩水电站的正常蓄水发电具有重要意义。采用该方法进行广域地质灾害高效快速动态监测,在第一时间发现隐患点并进行定性分析,为水库岸蓄水期因水位变化引起的突发性潜在地质灾害的识别提供了一种新思路。

关键词: 白鹤滩水库蓄水期短基线DInSAR滑坡隐患快速动态识别    
Abstract:

Baihetan Hydropower station is the second largest hydropower station in China after the Three Gorges Hydropower Station. With the rapid change of water level during the reservoir impoundment period, the geoenvironment of the reservoir area is changed, which is easy to cause sudden geological disasters. In addition, the maximum reservoir level of the transformed hydropower station is 825m, which has a large storage capacity and higher sudden-onset. In order to ensure the normal impoundment of the hydropower station and the safety of residents' life and property, it is necessary to quickly and dynamically identify geological hazards in the reservoir area. Therefore, based on the short baseline DInSAR method, this paper carried out the identification of geological hazards in the key reservoir bank section of Baihetan Reservoir area (Hulukou-Xiangbi Ling section) during the water storage period of the Jinsha River Basin. The sentinel-1A /B data were used for joint monitoring, and the re-entry period was increased to one scene every 6 days. Finally, a total of 66 scenes of data were obtained during the study area's water storage period from April 2021 to November 2021. DInSAR processing based on the same main image was performed combined with SRTM DEM data. The fast and dynamic deformation monitoring can be achieved by combination analysis of interference pair. Don't out of the 92 consensus exists significant deformation of strong deformation zone, are distributed in Jinsha River two sides, including eight strong deformation area have been inundated by the highest water level 800 m, 38 strong deformation area near the water, part of the small bank collapse, at the same time has chosen three continuous deformation area in combination with the actual topography analysis, found signs of deformation is more obvious, The location of strong deformation area is consistent with the monitoring results of DInSAR method, which proves the feasibility of using DInSAR method to quickly and dynamically discover new hidden points. The disaster analysis and monitoring and early warning during water storage period is of great significance to ensure the normal water storage and power generation of Baihetan Hydropower Station. The method provided in this paper can be used for large-scale, efficient, rapid and dynamic geological disaster monitoring, and the hidden danger points can be found in the first time and qualitative analysis can be carried out, which provides a new idea for the identification of potential geological disaster caused by the change of water level during the reservoir bank impoundment period.

Key words: Baihetan Reservoir    Impoundment period    Short baseline DInSAR    Landslide hazard    Fast dynamic identification
收稿日期: 2022-06-12 出版日期: 2023-11-07
ZTFLH:  P642.22  
基金资助: 四川省自然科学基金杰出青年科学基金项目(2023NSFSC1909);国家自然科学基金青年基金资助项目(41801391)
通讯作者: 戴可人     E-mail: wu_mt@hdec.com;daikeren17@cdut.edu.cn
作者简介: 吴明堂(1988-),男,河南民权人,工程师,主要从事地质灾害防治工作。E?mail:wu_mt@hdec.com
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引用本文:

吴明堂,房云峰,沈月,戴可人,姚义振,陈建强,冯文凯. 基于短基线DInSAR的白鹤滩库区蓄水期滑坡隐患广域快速动态识别[J]. 遥感技术与应用, 2023, 38(5): 1054-1061.

Mingtang WU,Yunfeng FANG,Yue SHEN,Keren DAI,Yizhen YAO,Jianqiang CHEN,Wenkai FENG. Fast Dynamic Identification of Landslide Hazards in Baihetan Reservoir area based on Short Baseline DInSAR Interferometry. Remote Sensing Technology and Application, 2023, 38(5): 1054-1061.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2023.5.1054        http://www.rsta.ac.cn/CN/Y2023/V38/I5/1054

图1  研究区域概况图
卫星轨道号

升/

降轨

重访周期/d分辨率

数量

/景

影像时间
Sentinel-1A26升轨125×20 m1820210409、20210421、20210503、20210515、20210527、20210608、20210620、20210702、20210714、20210726、20210807、20210819、20210831、20210912、20210924、20211006、20211018、20211030
Sentinel-1A128升轨125×20 m1720210416、20210428、20210510、20210522、20210603、20210615、20210627、20210709、20210721、20210802、20210814、20210826、20210907、20210919、20211001、20211013、20211025
Sentinel-1A62降轨125×20 m1720210411、20210423、20210505、20210517、20210529、20210610、20210622、20210704、20210716、20210728、20210809、20210821、20210902、20210914、20210926、20211008、20211020
Sentinel-1B62降轨125×20 m1420210405、20210417、20210429、20210511、20210523、20210604、20210628、20210710、20210722、20210827、20210920、20211002、2021114、2021126
表 1  SAR数据获取情况
图2  技术路线图
图3  隐患点识别概况图
图4  85号点干涉形变情况
图5  85号点形变现场图
图6  75、76、77号点形变情况
图7  公路断裂现场图
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