顾及共性误差的南极半岛地区连续GPS站坐标时间序列分析

发布时间：2018-07-31 12:38

【摘要】：共性误差是区域连续GPS网络中存在的一种与时空相关的主要误差源.对区域网络使用空间滤波能够有效减小这种误差并改善坐标时间序列的精度.本文使用GAMIT/GLOBK 10.5软件解算南极半岛区域11个GPS测站2010—2014的数据,各测站独立估计的单天解组成坐标时间序列.利用堆栈法(stacking)、主分量分析法(principal component analysis,简称PCA)和Karhunen-Loeve展开法(KLE)对其进行空间滤波,在利用PCA和KLE方法进行空间滤波时,使用前两个主分量作为共有模式分量.结果表明:三种方法均能有效提取南极半岛区域共性误差,PCA方法的滤波效果明显优于stacking方法,略微优于KLE方法;空间滤波能够有效减小残差时间序列的振幅、功率和RMS;对坐标时间序列进行空间滤波能够有效降低其线性项和周期项误差,从而提高线性项和周期项估计的精度.共性误差的谱分析结果显示在U方向上存在9.4天、13.7天等短周期的信号,推测共性误差组成成分中可能含有与海潮相关的误差源.
[Abstract]:The common error is one of the main error sources related to time and space in regional continuous GPS networks. Spatial filtering can effectively reduce this error and improve the precision of coordinate time series. In this paper, GAMIT/GLOBK 10.5 software is used to calculate the data of 11 GPS stations in the Antarctic Peninsula region from 2010 to 2014. The single day solution independently estimated by each station constitutes the coordinate time series. The stack (stacking), principal component analysis (principal component analysis,) and the Karhunen-Loeve expansion method (KLE) are used for spatial filtering. When PCA and KLE are used for spatial filtering, the first two principal components are used as common mode components. The results show that the three methods can effectively extract the common errors of Antarctic Peninsula region. The filtering effect of PCA method is obviously better than that of stacking method and slightly better than that of KLE method, and spatial filtering can effectively reduce the amplitude of residual time series. The spatial filtering of coordinate time series can effectively reduce the errors of linear and periodic terms and thus improve the accuracy of linear and periodic term estimation. The spectral analysis of common errors shows that there are 9.4 days and 13.7 days of short period signals in U direction. It is speculated that the components of common errors may contain the error sources related to the sea tide.
【作者单位】：武汉大学中国南极测绘研究中心;武汉大学测绘遥感信息工程国家重点实验室;
【基金】：国家自然科学基金重点项目(41531069);国家自然科学基金项目(41174019,41176173) 国家重大科学研究973计划(2012CB957701,2013CBA01804) 南北极环境综合考察及资源潜力评估专项(CHINARE2015)联合资助
【分类号】：P228.4

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