赵亚丽, 王彦兵, 王新雨, 田秀秀, 李小娟, 余洁. 利用TPCA分析北京平原区地面沉降的时空演化特征[J]. 武汉大学学报 ( 信息科学版), 2022, 47(9): 1498-1506. DOI: 10.13203/j.whugis20200721
引用本文: 赵亚丽, 王彦兵, 王新雨, 田秀秀, 李小娟, 余洁. 利用TPCA分析北京平原区地面沉降的时空演化特征[J]. 武汉大学学报 ( 信息科学版), 2022, 47(9): 1498-1506. DOI: 10.13203/j.whugis20200721
ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506. DOI: 10.13203/j.whugis20200721
Citation: ZHAO Yali, WANG Yanbing, WANG Xinyu, TIAN Xiuxiu, LI Xiaojuan, YU Jie. Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA[J]. Geomatics and Information Science of Wuhan University, 2022, 47(9): 1498-1506. DOI: 10.13203/j.whugis20200721

利用TPCA分析北京平原区地面沉降的时空演化特征

Temporal and Spatial Analysis of Land Subsidence in Beijing Plain Based on TPCA

  • 摘要: 时间主成分分析(temporal principal component analysis,TPCA)可用于地学领域中提取时空数据的时序特征和空间分布特征,北京平原区的地面沉降具有典型的时序和空间特征。在利用永久散射体干涉测量技术获取的北京平原区2003—2010年地面沉降数据的基础上,采用TPCA方法分析了北京平原区地面沉降时空演化特征。经分析发现:(1)TPCA分析得到的第一主成分反映了地面沉降在该长时序阶段的空间分布特征。(2)第二主成分得分为正的空间点与可压缩层厚度在130 m以上的区域在空间分布上有一致性和相关性。(3)在空间上,第一主成分为负值与第二主成分为正值的永久散射体点分布在年均沉降速率30 mm/a以上的严重沉降区域。严重沉降区具有明显的南北沉降分类现象和季节性差异,具体表现为:北部沉降区在春夏季节的沉降量大于秋冬季节;南部沉降区则与之相反。总之,基于时间主成分分析方法可分析得到研究区的地面沉降时空演化规律,为城市安全监测提供数据支撑。

     

    Abstract:
      Objectives  Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discov‍ered simultaneously. Temporal principal component analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal and spatial data in the field of geosciences. The land subsidence in the Beijing plain has typical temporal and spatial characteristics.
      Methods  (1) Permanent scatterer interferometric synthetic aperture radartechnique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model.(2)Based on the land subsid‍ence of about 100 000 permanent scatterer (PS) points and 51-time series, we construct the original data matrix X100 000×51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing plain.
      Results  It is found that: (1) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (2) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130 m. (3) ‍The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30 mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsid‍ence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than that of in autumn and winter, it is an opposite variation in the southern subsidence area.
      Conclusions  In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitor‍ing by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. Principal component analysis(PCA) only uses the second‍-‍order statistical information of the original data and ignores its high‍-‍order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components.

     

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