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.
LIU Xiaoxiang,GAO Ertao,LUO Yi et al. Analysis of Coordinate Time Series of CMONOC GNSS Fiducial Stations Using Principal Component Analysis[J]. jgg, 2021, 41(1): 43-48.
LIU Xiaoxiang,GAO Ertao,LUO Yi et al. Analysis of Coordinate Time Series of CMONOC GNSS Fiducial Stations Using Principal Component Analysis[J]. jgg, 2021, 41(1): 43-48.