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联合InSAR与PCA的北京平原地面沉降时空分析
引用本文:何 旭,何 毅,张立峰,陈 毅,蒲虹宇,陈宝山. 联合InSAR与PCA的北京平原地面沉降时空分析[J]. 光谱学与光谱分析, 2022, 42(7): 2315-2324. DOI: 10.3964/j.issn.1000-0593(2022)07-2315-10
作者姓名:何 旭  何 毅  张立峰  陈 毅  蒲虹宇  陈宝山
作者单位:兰州交通大学测绘与地理信息学院,甘肃 兰州 730070;地理国情监测技术应用国家地方联合工程研究中心,甘肃 兰州 730070;甘肃省地理国情监测工程实验室,甘肃 兰州 730070
基金项目:中国博士后科学基金面上基金项目(2019M660092XB),甘肃省科技计划项目(20JR2RA002),甘肃省自然科学基金项目(20JR10RA249),甘肃省青年科学基金项目(20JR10RA272),兰州交通大学-天津大学创新基金项目(2020055),兰州交通大学优秀平台(201806)资助
摘    要:20世纪70年代以来,由于地下水超采、可压缩层厚度不均等引起的不均匀地面沉降已逐渐发展成为北京平原最严重的地质灾害之一。目前,北京平原最新时段的地面沉降时空分析的研究报道较少。根据主动微波获取的39景Sentinel-1A光谱影像,使用SBAS-InSAR技术,获取了北京平原2017年5月—2020年5月的地面形变数据;利用主成分分析法对北京平原的地面沉降时空特征进行了分析。在SBAS-InSAR技术处理中设置时间基线120天、空间基线的阈值设置为最大临界基线的45%,生成154个干涉对;对所有干涉对进行配准、干涉,对干涉后的结果进行去平、使用Goldstein算法进行滤波、生成相干系数图并使用最小费用流算法进行相位解缠,筛选73个优质的干涉对进行轨道精炼与重去平,以估算和去除残余相位;通过时间高通滤波、空间低通滤波去除大气相位;最后采用最小二乘法和奇异值分解获取北京平原2017年5月—2020年5月的地面形变数据。在监测时间段内平均沉降速率最大为114.9 mm·yr-1,最大累积沉降量为345.9 mm·yr-1,均位于朝阳金盏。2019...

关 键 词:北京平原  SBAS-InSAR  地面沉降  主成分分析  南水进京
收稿时间:2021-06-30

Spatio-Temporal Analysis of Land Subsidence in Beijing Plain Based on InSAR and PCA
HE Xu,HE Yi,ZHANG Li-feng,CHEN Yi,PU Hong-yu,CHEN Bao-shan. Spatio-Temporal Analysis of Land Subsidence in Beijing Plain Based on InSAR and PCA[J]. Spectroscopy and Spectral Analysis, 2022, 42(7): 2315-2324. DOI: 10.3964/j.issn.1000-0593(2022)07-2315-10
Authors:HE Xu  HE Yi  ZHANG Li-feng  CHEN Yi  PU Hong-yu  CHEN Bao-shan
Affiliation:1. Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China2. National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring, Lanzhou 730070, China3. Gansu Provincial Engineering Laboratory for National Geographic State Monitoring, Lanzhou 730070, China
Abstract:Since the 1970s, uneven ground subsidence caused by groundwater overdraft and uneven thickness of compressible layers has gradually developed into one of the most serious geological hazards in Beijing Plain. There are few research reports on the temporal and spatial analysis of land subsidence in the latest period in Beijing Plain. Therefore, this paper used SBAS-InSAR technology to obtain the ground deformation data of the Beijing Plain from May 2017 to May 2020 based on 39 Sentinel-1A spectral images acquired by active microwaves. The principal component analysis method was used to analyse land subsidence’s temporal and spatial characteristics in the Beijing Plain. In the SBAS-InSAR technology processing, the time baseline was set to 120 days, and the threshold of the space baseline was set to 45% of the maximum normal baseline, and 154 interference pairs were generated, and then all interference pairs were registered and interfered. After the interference, the results were flattened, Goldstein flattening for filtering, generated coherence coefficient and used minimum cost flow algorithm for phase unwrapping. The 73 high-quality interference pairs were screened for orbit refinement and re-leveling to estimate and remove the residual phase. The atmospheric phase was removed through temporal high-pass filtering and spatial low-pass filtering. Finally, the least-squares method and singular value decomposition were used to obtain land subsidence data of the Beijing Plain from May 2017 to May 2020. During the monitoring period, the maximum average deformation rate was -114.9 mm·yr-1, and the maximum cumulative subsidence was 345.9 mm. which was located in Chaoyang Jinzhan. Compared with 2018, the ranges where the settlement rate of the central plain decreased by exceeding 50 mm·yr-1 in 2019 in the Haidian District, Changping district, but the settlement rate range of Daxing District was gradually increasing. It analysed the land subsidence of the Beijing Plain by using the principal component analysis method. It was concluded that the first three principal components explained 99.11% of the data characteristics. The first principal component explained 96.48% of the characteristics, reflecting the long-term and unrecoverable process of groundwater subsidence caused by long-term groundwater extraction. However, the replenishment of groundwater by the South Water entering Beijing slowed down the settlement rate in the central plain. The second principal component explained the 2.11% feature, highlighting the interannual settlement process, which was related to factors such as the thickness of the compressible layer and the type of land use; The third principal component explained 0.52% of the characteristics of the data set and emphasized the seasonal elastic deformation regulated by rainfall. The research results of this paper can provide a certain scientific basis for the comprehensive management of land subsidence in the Beijing Plain.
Keywords:Beijing Plain  SBAS-InSAR  Land subsidence  Principal component analysis  South water into Beijing  
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