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The coordinate time series analysis of continuous GPS stations in the Antarctic Peninsula with consideration of common mode

发布日期:2016-11-30 09:45:37 阅读次数:[1637]次 作者:

核心提示:来源出版物: CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION

标题: The coordinate time series analysis of continuous GPS stations in the Antarctic Peninsula with consideration of common mode error
作者: Ma, C (Ma Chao); Li, F (Li Fei); Zhang, SK (Zhang Sheng-Kai); Lei, JT (Lei Jin-Tao); E, DC (E Dong-Chen); Hao, WF (Hao Wei-Feng); Zhang, QC (Zhang Qing-Chuan)
来源出版物: CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION  卷: 59  期: 8  页: 2783-2795  DOI: 10.6038/cjg20160806  出版年: AUG 2016
摘要: The spatially and temporally correlated common mode errors (CME) are the dominant error sources in daily GPS solutions in the regional CGPS network. Spatial filtering is an effective way to improve the precision of coordinate time series for regional CGPS networks by reducing these errors. The data of 11 GPS stations from 2010 to 2014 in Antarctic Peninsula are processed with GAMIT/GLOBK10.5 software. The set of individually estimated daily positions then make up the position time series for every stations. Three filtering approaches including stacking, principal component analysis (PCA) and Karhunen-Loeve expansion (KLE) were applied to daily coordinate time series of Antarctic Peninsula GPS network from 2010 to 2014. The first two principal components in PCA and KLE were used as common modes. The filtering results show that all of the three methods can effectively extract the common mode errors in the Antarctic Peninsula, but the results of PCA are much better than those of stacking, and slightly better than those of KLE. We demonstrate that spatial filtering can effectively reduce the amplitude and power of the residual time series and effectively reduce the errors of linear term and periodic term in coordinate time series, so as to improve the accuracy of their estimations. The spectrum results of common mode errors from PCA analysis show that 9.4-day and 13.7-day period signals in the vertical component might be related to the ocean tide model used in the analysis.


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