InSAR技术和孕灾背景指标相结合的地灾隐患识别

董佳慧; 牛瑞卿; 亓梦茹; 丁赞; 徐航; 何睿

doi:10.19509/j.cnki.dzkq.2022.0024

InSAR技术和孕灾背景指标相结合的地灾隐患识别

doi: 10.19509/j.cnki.dzkq.2022.0024

董佳慧^1,,
牛瑞卿^1, ,,
亓梦茹¹,
丁赞²,
徐航²,
何睿²

基金项目:

2019年中央自然灾害防治体系建设补助资金项目"恩施州重大隐患区区域综合遥感监测与隐患识别" HBYM-GTJ-2020-GK-1002

中国地质调查局武汉地质调查中心项目"三峡库区专业监测建设和预警分析指导" 0001212012AC50021

详细信息

作者简介:
董佳慧(1996—), 女, 现正攻读地球探测与信息技术专业博士学位, 主要从事地灾遥感和环境遥感研究。E-mail: ivy.dong@cug.edu.cn

通讯作者:
牛瑞卿(1969—), 男, 教授, 主要从事3S和地质灾害研究工作。E-mail: rqniu@163.com

中图分类号: X87
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-13

Identification of geological hazards based on the combination of InSAR technology and disaster background indicators

Dong Jiahui^1
,,
Niu Ruiqin^{1
, ,},
Qi Mengru¹,
Ding Zan²,
Xu Hang²,
He Rui²

摘要

摘要: 合成孔径雷达干涉测量(interferometric synthetic aperture radar, 简称InSAR)是获取地表形变的重要手段, 由于InSAR数据获取的限制和数据处理中产生的精度误差等问题, 在地灾隐患识别方面的工作还需要联合地质灾害本身进行分析。为此提出了一种基于InSAR技术与研究区孕灾背景指标相结合的方法, 并将其应用于三峡库区巴东段的地灾隐患识别研究中。研究结果表明, 使用ALOS-2 PALSAR雷达影像, 应用时序InSAR技术得到了研究区的变形空间分布和变化速率, 并结合研究区的孕灾背景, 将易发性等级、坡度、工程岩组和是否与灾害目录重叠4个指标作为地灾隐患判别的指标, 综合识别出19处疑似地灾隐患区, 然后对疑似地灾隐患区进行了逐一野外核查, 经验证地灾隐患识别成功率为78.9%。研究成果证明了将InSAR技术和孕灾背景相结合进行地灾隐患识别方法的可行性, 可在区域灾害识别中发挥重要作用。
- 地质灾害隐患识别 /
- InSAR /
- ALOS-2 /
- 孕灾背景指标 /
- 巴东
Abstract: Synthetic aperture radar interferometry (InSAR) is an important method to obtain surface deformation information. Due to the limitations of InSAR data acquisition and the accuracy errors produced in the data processing, the identification of hidden dangers also needs to be combined with the analysis of geological hazards themselves, so a method based on InSAR technology combined with the disaster-pregnancy background in the study area is proposed. This study took the Badong section of the Three Gorges Reservoir area as the study area, and ALOS-2 PALSAR radar images were used to obtain the spatial distribution and change rate of deformation in the study area by using time-series InSAR technology. Combining the disaster-prone background of the study area, four indicators of the susceptibility level, slope, engineering rock group and distance from the disaster catalogue point are used as indicators for the identification of hidden dangers of geological disasters. As a result, 19 suspected hidden disaster areas were identified comprehensively, and then the suspected hidden geological disaster areas were verified in the field one by one. The success rate of verification and identification was 78.9%, proving that the method combining the disaster pregnancy background and InSAR results is feasible and can play an important role in regional disaster identification.
- recognition of hidden geological hazards /
- InSAR /
- ALOS-2 /
- disaster background indicators /
- Badong

HTML全文

图 1 研究区位置

Figure 1. Location map of the study area

下载: 全尺寸图片幻灯片

图 2 时空基线图

1~8为影像编号

Figure 2. Spatial and temporal baselines of SAR datasets

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图 3 雷达视线方向的年均形变速率

Figure 3. Average variable speed of deformation along the radar line of sight

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图 4 因子分级图

Figure 4. Hierarchical graphs of factors

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图 5 易发性分区图

Figure 5. Zoning map of susceptibility using the CNN-LSTM model

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图 6 研究区内已知灾害点坡度柱状图

Figure 6. Slope histogram of disasters known in the study area

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图 7 地灾隐患区分布图

Figure 7. Distribution map of potential hazard areas

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图 8 P2变形区InSAR监测形变速率(a)及现场照片(b, c)

a.雷达视线向形变速率; b.地面裂缝现场照片; c.路基裂缝

Figure 8. InSAR monitoring deformation rate (a) and on-site photos (b, c) of the deformation zone P2

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图 9 累积形变量曲线图

Figure 9. Curve of cumulative deformation

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表 1 ALOS-2数据成像时间表

Table 1. Timeline of ALOS-2 digital imaging

序号	成像时间
1	2020/04/11
2	2020/05/23
3	2020/06/20
4	2020/07/04
5	2020/08/01
6	2020/09/12
7	2020/10/10
8	2020/11/07

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表 2 地灾隐患区判别指标情况

Table 2. Conditions of evaluation indicators of potential dazard areas

编号	判别指标				判别指标个数
编号	易发性等级	坡度/(°)	工程岩组	是否与灾害目录重叠	判别指标个数
P1	极高易发	10~20	较软	是	4
P2	极高易发	20~30	较软	是	4
P3	高易发	10~20	极软	否	3
P4	极高易发	20~30	极软	否	3
P5	极高易发	20~30	坚硬	是	3
P6	高易发	10~20	极软	是	4
P7	极高易发	20~30	较软	是	4
P8	极高易发	20~30	极软	否	3
P9	中易发	20~30	软岩	是	4
P10	中易发	30~40	软岩	是	4
P11	中易发	10~20	软岩	否	3
P12	中易发	30~40	软岩	是	4
P13	极高易发	20~30	较软	是	4
P14	极高易发	20~30	坚硬	是	3
P15	极高易发	10~20	坚硬	是	3
P16	高易发	20~30	较软	是	4
P17	极高易发	10~20	较软	是	4
P18	极高易发	10~20	坚硬	是	3
P19	极高易发	10~20	坚硬	否	2

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表 3 潜在地灾点位及变形特征统计

Table 3. Statistics table of potential hazard sites and deformation characteristics

编号	最大沉降速率/ (mm·a^-1)	经度	纬度	变形特征	证实灾种
P1	-156.746	110° 23' 10.470" E	31° 2' 30.743" N	公路发育多条沉降变形裂缝，已废弃建筑物墙开裂，公路变形长度约10 m	滑坡
P2	-186.597	110° 27' 11.149" E	31° 1' 57.373" N	道路存在裂缝，走向220°，深度约40 cm	滑坡
P3	-166.277	110° 26' 1.313" E	31° 3' 40.856" N	可见公路发育弧形裂缝，走向195°，缝宽约1 cm，长约8 m	滑坡
P4	-203.549	110° 26' 31.105" E	31° 4' 29.182" N	公路存在多处弧形裂缝(2020年)，走向266°，宽约3 cm，长约5 m，最深处约10 cm	滑坡
P5	-164.861	110° 24' 5.021" E	31° 5' 2.746" N	公路前缘，橘树发育下挫裂缝，走向188°，长约8 m，最大下挫深度约0.5 m，呈直线型，缝宽1~2 cm，多级干砌石挡墙存在局部垮塌	滑坡
P6	-106.729	110° 24' 43.590" E	31° 3' 20.615" N	道路存在裂缝，走向308°，宽约2 cm，地表下座0.5 m，东瀼口村9组38号，经调查为煤矿开采引起地面下沉	地面沉降
P7	-176.093	110° 25' 32.555" E	31° 2' 8.815" N	变形区西侧为冲沟，坡度约50°，坡面存在大量松散堆积体；变形区东侧为道路切坡，坡高2~4 m	滑坡
P8	-262.390	110° 27' 19.755" E	31° 3' 14.473" N	该处主要为农田及林地，表层土体结构松散	非地灾
P9	-164.571	110° 26' 7.869" E	31° 1' 25.261" N	未见公路及斜坡变形裂缝，位于沿江处，考虑为水体对InSAR的影响	非地灾
P10	-238.687	110° 24' 28.435" E	31° 1' 36.568" N	位于茶麻沟泥石流附近，为沿公路的林地	滑坡
P11	-130.077	110° 27' 12.314" E	31° 1' 17.071" N	顺公路方向出现裂缝，宽1~2 mm，长约15 m，外沿开裂，斜坡较陡	滑坡
P12	-123.997	110° 25' 5.682" E	31° 1' 30.042" N	位于红龙坪滑坡附近	滑坡
P13	-130.387	110° 21' 18.590" E	31° 3' 4.134" N	位于已知隐患点附近，在长江南岸公路边坡，有锚杆治理护坡，可见变形监测墩	滑坡
P14	-110.139	110° 18' 54.160" E	31° 3' 23.752" N	位于杜公祠滑坡附近	滑坡
P15	-89.087	110° 21' 19.359" E	31° 3' 51.381" N	居民房前见裂缝，走向198°，长约12 m，宽1~5 m，房前地坪开裂，地坪前挡墙存在明显鼓胀变形和多条平行裂缝，公路内侧干砌石挡墙变形	滑坡
P16	-150.895	110° 21' 50.151" E	31° 2' 11.801" N	公路内测有治理的高陡边坡，为已知隐患点	滑坡
P17	-127.195	110° 23' 22.820" E	31° 3' 53.268" N	坡面种植橘树，坡度大，降雨时坡面汇水易使水土流失，坡面覆盖层为粉质黏土夹碎石	非地灾
P18	-124.919	110° 20' 26.353" E	31° 3' 34.400" N	该点为太矶头集镇垃圾处理填埋场，由于填埋生活垃圾，局部挖填取土	非地灾
P19	-106.789	110° 21' 11.969" E	31° 4' 18.647" N	现场为一施工工地，大量抗滑桩正在建设，地表土体有明显的施工开挖迹象，土层厚6~8 m。坐标点北侧房屋前于2020年4-5月发生滑移	滑坡

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