| 利用主成分分析法分析GNSS坐标时间序列 |
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| 引用本文： | 刘晓祥,高二涛,罗益,付波霖.利用主成分分析法分析GNSS坐标时间序列[J].大地测量与地球动力学,2021,41(1):43-48. |
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| 作者姓名： | 刘晓祥 高二涛 罗益 付波霖 |
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| 作者单位： | 重庆市永川区规划和自然资源局,重庆市人民北路6号,402160;桂林理工大学测绘地理信息学院,桂林市雁山街319号,541006;桂林理工大学测绘地理信息学院,桂林市雁山街319号,541006;桂林理工大学测绘地理信息学院,桂林市雁山街319号,541006 |
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| 基金项目： | 广西空间信息与测绘重点实验室基金;广西自然科学基金;国家自然科学基金 |
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| 摘 要： | 利用主成分分析法对陆态网224个GNSS基准站坐标时间序列进行分析。首先对基准站原始坐标序列进行突变项拟合、粗差剔除、缺失数据插值补齐等预处理；然后对预处理后的站点残差坐标时间序列分N、E、U方向组建时间序列矩阵进行主成分分析，根据各方向主分量及其相应的空间特征向量分析站点空间响应分布特征、共模误差以及异常站点的影响。结果表明，仅通过第1主分量不能准确体现共模误差的时空特点，因此将前3个主分量纳入共模误差分析；华北地区、西北地区以及云南地区各方向主分量显示出相对一致的空间响应分布，可能是水储量变化导致的；对比剔除异常站点前后的PAC结果发现，N、E、U方向第1、2主分量的贡献率变化明显，U方向表现最为显著，其中第1主分量贡献率分别提高2.0% (N)、3.9% (E)、5.7% (U)，第2主分量则分别下降1.1% (N)、1.9% (E)、6.7% (U)，剔除异常站点后，站点的空间响应得到明显提高。
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| 关 键 词： | 陆态网 GNSS坐标时间序列 主成分分析 共模误差 异常站点 |
Analysis of Coordinate Time Series of CMONOC GNSS FiducialStations Using Principal Component Analysis |
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| LIU Xiaoxiang,GAO Ertao,LUO Yi,FU Bolin.Analysis of Coordinate Time Series of CMONOC GNSS FiducialStations Using Principal Component Analysis[J].Journal of Geodesy and Geodynamics,2021,41(1):43-48. |
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| Authors: | LIU Xiaoxiang GAO Ertao LUO Yi FU Bolin |
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| Institution: | (Planning and Natural Resources Bureau of Yongchuan,6 North-Renmin Road,Chongqing 402160,China;College of Geomatics and Geoinformation,Guilin University of Technology,319 Yanshan Street,Guilin 541006,China) |
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| Abstract: | We use principal component analysis(PCA) to analyze the coordinate time series of 224 GNSS reference stations of CMONOC. First, the original coordinate sequence of the reference station is preprocessed by mutation fitting, gross error elimination and missing data interpolation. Then, we performed PCA separately on the continuous residual GNSS coordinate time series matrix to calculate principal components(PCs) and corresponding spatial eigenvectors in three directions: N,E and U. According to the PCs of each direction and their corresponding spatial eigenvectors, we analyze the common mode error(CME), regional distribution characteristics of sites spatial response, and abnormal site impact on PCA results. The results indicate that a single PC is no longer able to reflect the whole spatial and temporal patterns of the CME in China; the first three PCs are required to be considered to analyze the CME. In addition, there are relatively uniform spatial responses in the northwest region, north China and Yunnan province, which imply water reserves vary significantly. After removing the abnormal sites, the first two PCs, especially the vertical direction, exhibit obvious variations in contribution and spatial response. The contribution rate of the first PCs increased by 2.0% (N), 3.9% (E) and 5.7% (U) respectively, while the second PCs decreased by 1.1% (N), 1.9% (E) and 6.7% (U) respectively. The spatial response of the station is significantly improved after the removal of abnormal sites. |
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| Keywords: | CMONOC GNSS coordinate time series principal component analysis common mode error abnormal sites |
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