基于时间序列InSAR技术的川东红层滑坡识别及变形规律
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摘要
针对川东红层滑坡典型易发区域,采用小基线集(SBAS)时间序列的InSAR(interferometry synthetic aperture radar)方法进行了解译,并采用Google Earth可视化展示,有效圈定并复核了潜在隐患区域。提取了采矿引起斜坡失稳及牛马场滑坡的变形位移曲线,揭示了其变形规律。研究结果表明:解译区滑坡基本都发育在形变速率较大的黄色欠稳定和红色不稳定区域,区域形变位移值大小与滑坡发育比例成正比关系,35%的解译面积滑坡发育比率可以达到70%以上。同时通过野外调查发现4个潜在不稳定隐患斜坡,极大地提高了红层滑坡的早期识别能力。InSAR解译显示研究区西北区由采矿引起的斜坡失稳,与干涉图上的条纹位置对应较为一致,形变位移最大能达到视线向20 mm/a;牛马场滑坡范围均为垂直形变速率较大的黄色和红色解译点,变形位移曲线显示滑坡发生前已经处于蠕变变形阶段,累计变形量可以达到500 mm左右。
Aim at typical area of landslide in eastern Sichuan,the time series InSAR (interferometry synthetic aperture radar) method of small baseline (SBAS) was used to interpretation,and the Google Earth visualization was also used to delineate and review potential areas effectively. The deformation and displacement curves of the Niumachang landslide were extracted,and its deformation was revealed. The results show as follows. The landslides in the interpretation are basically developed in the yellow and red unstable area region. The deformation and displacement value of the region is proportional to the proportion of the development of the landslide,and the rate of 35%landslides in the interpretation area can reach more than 70%. and 4 unstable hidden landslides have been found,which greatly improves the early recognition ability of the redbed landslide. The InSAR interpretation shows slope instability caused by mining in the northwest area of the study area. which consistent with the stripe position on the interferogram. the maximum deformation displacement reaches 20 mm/a. The range of Niumachang landslide area shows yellow and red interpretation point with larger vertical deformation rate,the deformation displacement curve shows that the landslide has already been in the slow deformation stage before the occurrence,and the cumulative deformation can reach about 500 mm.
Aim at typical area of landslide in eastern Sichuan,the time series InSAR (interferometry synthetic aperture radar) method of small baseline (SBAS) was used to interpretation,and the Google Earth visualization was also used to delineate and review potential areas effectively. The deformation and displacement curves of the Niumachang landslide were extracted,and its deformation was revealed. The results show as follows. The landslides in the interpretation are basically developed in the yellow and red unstable area region. The deformation and displacement value of the region is proportional to the proportion of the development of the landslide,and the rate of 35%landslides in the interpretation area can reach more than 70%. and 4 unstable hidden landslides have been found,which greatly improves the early recognition ability of the redbed landslide. The InSAR interpretation shows slope instability caused by mining in the northwest area of the study area. which consistent with the stripe position on the interferogram. the maximum deformation displacement reaches 20 mm/a. The range of Niumachang landslide area shows yellow and red interpretation point with larger vertical deformation rate,the deformation displacement curve shows that the landslide has already been in the slow deformation stage before the occurrence,and the cumulative deformation can reach about 500 mm.
引文
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14单新建,张国宏,汪驰升,等.基于INSAR和GPS测数据的尼泊尔地震发震断层特征参数联合反演研究[J].地球物理学报,2015,58(11):4266-4276Shan Xinjian,Zhang Guohong,Wang Chisheng,et al.Joint inversion for the spatial fault slip distribution of the 2015 Nepal MW 7.9earthquake based on INSAR and GPS observation[J].Chinese Journal of Engineering Geophysics,2015,58(11):4266-4276
15单新建,屈春燕,郭利民,等.基于INSAR和GPS观测的汶川同震垂直形变场的获取[J].地震地质,2014,36(3):719-729Shan Xinjian,Qu Chunyan,Guo Liming,et al.The vertical coseismic deformation field of the Wenchuan earthquake based on the combination of based on INSAR and GPS observation[J].Seismology and Geology,2014,36(3):719-729
16 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):2375-2383
2胡泽铭.四川红层地区缓倾角滑坡成因机理研究[D].成都:成都理工大学,2013Hu Zeming.Study on formation mechanism of the slow-inclination landslide in Sichuan red stratum region[D].Chengdu:Chengdu University of Technology,2013
3周志华,林维芳,许高程.基于面向对象的滑坡快速识别技术研究[J].安徽农业科技,2012,40(5):3017-3018Zhou Zhihua,Lin Weifang,Xu Gaocheng.Research of fast landslide recognition based on object-oriented[J].Technology Journal of Anhui Agricultural Sciences,2012,40(5):3017-3018
4叶润青,邓清禄,王海庆,等.基于图像分类方法滑坡识别与特征提取---以归州老滑坡为例[J].工程地球物理学,2007,4(6):575-577Ye Runqing,Deng Qinglu,Wang Haiqing,et al.Landsides identification based on image classification:a case study on landslide on Guizhoulaocheng landslide in the Three Gorges reservoir area[J].Chinese Journal of Engineering Geophysics,2007,4(6):575-577
5冯光胜.LIDAR在地质灾害调查与监测中的应用研究[J].铁道工程学报,2014,190(7):13-16Feng Guangsheng.Application of LIDAR technology in geological hazard investigation and monitoring[J].Journal of Railway Engineering Society,2014,190(7):13-16
6于海洋,牛峰明,罗玲.一种综合机载LIDAR与高分辨率航空影像的铁路轨道提取方法[J].测绘工程,2016,5(5):43-46Yu Haiyang,Niu Fengming,Luo Ling.Rail extraction from airborne LIDAR and high resolution aerial image[J].Engineering of Surveying and Mapping,2016,5(5):43-46
7 Glenn N F,Chadwick J S D.Analysis of Li DAR-derived topographic information for characterizing and differentiating landslide morphology and activity[J].Geomorphology,2006,73(1-2):131-148
8 Schulz W H.Landslide susceptibility revealed by Li DAR imagery and historical records Seattle Washington[J].Engineering Geology.2007,89(1-2):67-87
9 Booth A M,Roering J J,T P J.Automated landslide mapping using spectral analysis and high-resolution topographic data:Puget Sound lowlands,Washington,and Portland Hills,Oregon[J].Geomorphology,2009,109(3-4):132-147
10王桂杰,谢谟文,邱骋.D-INSAR技术在大范围滑坡监测中的应用[J].岩土力学,2010,31(4):1338-1343Wang Guijie,Xie Mowen,Qiu Cheng.Application of D-INSARtechnique to landslide monitoring[J].Rock and Soil Mechanics,2010,31(4):1338-1343
11 Xia Y,Kaufmann H,Guo X F.Landslide monitoring in the Three Gorges area using D-INSAR and corner reflectors[J].Photogrammetric Engineering&Remote Sensing,2004,70(10):1167-1172
12张建龙,Vernon H Singhroy.差分干涉测量技术在四川甲居滑坡监测中应用研究[J].成都理工大学学报(自然科学版),2010,37(5):554-557Zhang Jianlong,Vernon H Singhroy.Application of differential interferometric SAR to the Jiaju landslide in Sichuan[J].Journal of Chengdu University of Technology(Natural Science Edition),2010,37(5):554-557
13孙晓鹏,李亮,应国伟.利用Google-earth的PS-INSAR地表形变监测可视化展示方法[J].测绘地理信息,2018,43(1):101-104Sun Xiaopeng,Li Liang,Ying Guowei.PS-INSAR ground deformation monitoring results visualization based on Google-earth[J].Journal of Geomatics,2018,43(1):101-104
14单新建,张国宏,汪驰升,等.基于INSAR和GPS测数据的尼泊尔地震发震断层特征参数联合反演研究[J].地球物理学报,2015,58(11):4266-4276Shan Xinjian,Zhang Guohong,Wang Chisheng,et al.Joint inversion for the spatial fault slip distribution of the 2015 Nepal MW 7.9earthquake based on INSAR and GPS observation[J].Chinese Journal of Engineering Geophysics,2015,58(11):4266-4276
15单新建,屈春燕,郭利民,等.基于INSAR和GPS观测的汶川同震垂直形变场的获取[J].地震地质,2014,36(3):719-729Shan Xinjian,Qu Chunyan,Guo Liming,et al.The vertical coseismic deformation field of the Wenchuan earthquake based on the combination of based on INSAR and GPS observation[J].Seismology and Geology,2014,36(3):719-729
16 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):2375-2383