基于PCA提取GPS序列共模误差分析与应用
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- 英文论文题名：Analysis and Application of Extracting GPS Time Series Common Mode Errors Based on PCA
- 论文作者：高涵
- 英文论文作者：Gao Han ; Yunnan earthquake administration
- 年：2016
- 作者机构：云南省地震局形变测量中心;
- 论文关键词：GPS时间序列 ; 共模误差 ; 主成分分析 ; 速度场
- 英文论文关键词：GPS time series ; common mode error ; principal component analysis ; velocity field
- 会议召开时间：2016-11-01
- 会议录名称：云南省测绘地理信息学会2016年学术年会论文集
- 语种：中文
- 分类号：P228.4
- 学会代码：YNKX
- 会议名称：云南省测绘地理信息学会2016年学术年会
- 会议地点：中国云南昆明
- 主办单位：云南省测绘地理信息局、云南省测绘地理信息学会
- 学会名称：云南省科学技术协会
- 页数：9
- 文件大小：2772k
- 原文格式：D
- 会议级别：地方
摘要
基于美国中南部GPS连续运行参考站网络2006~2011年五年的观测资料,采用主成分空间滤波PCA算法提取该区域网GPS站点时间序列的共模误差,使站点时间序列的精度和可靠性得到提高,并进一步讨论了共模误差对该区域速度场的影响。结果证实,在提取地壳微动态形变信息时不容忽视共模误差的存在,本文所涉及的PCA方法能有效的剔除共模误差,提高结果的可靠性。
Characteristics of daily position time series which dated from 2006 to 2011 in South Central of America GPS fiducial network are researched in this paper. A spatial filtering algorithm based on principal component analysis was employed to extract and remove the common mode errors from the coordinate time series. This method promoted the accuracy and reliability of the sites coordinate. Further, we discussed the differences influence in velocity field of GPS stations by test to wipe off the common-mode error. The result shows that the common mode errors can not be neglected in time series analysis, particularly when we deal with the micro deformation, the PCA method can get rid of the common mode errors effectively and improve the reliability of result.
Characteristics of daily position time series which dated from 2006 to 2011 in South Central of America GPS fiducial network are researched in this paper. A spatial filtering algorithm based on principal component analysis was employed to extract and remove the common mode errors from the coordinate time series. This method promoted the accuracy and reliability of the sites coordinate. Further, we discussed the differences influence in velocity field of GPS stations by test to wipe off the common-mode error. The result shows that the common mode errors can not be neglected in time series analysis, particularly when we deal with the micro deformation, the PCA method can get rid of the common mode errors effectively and improve the reliability of result.
引文
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[2]Dong D N,Fang P,Bock Y,Webb F,Prawi-rodirdjo L,Kedar S,Jamason P.Satiotemporal filtering using principal component analysis and Karhunen-Loeve expansion approaches for regi-onal GPS network analysis[J].J.Geophys Res,2006,111:B03405
[3]Tian Yunfeng,Shen Zhengkang.Progress on reduction of non-tectonic noise in GPS position time series[J].Acta Seismologica Sinica,2009,31(1):68--81.田云锋,沈正康.GPS坐标时间序列中非构造噪声的剔除方法研究进展[J],地震学报,2009,31(1):68-81
[4]Zhang J,et al.Southem California permanent GPS geodeticarray.Error analysis of daily position estimates and site velocities[J].J.Geophys Res,1997,102.18035-18055
[5]Tian Yunfeng,Shen Zhengkang.Correlation weighted stacking filtering of common-mode component in GPS observation network[J].Acta Seismologica Sinica,2011,33(2):198--208.田云峰,沈正康.GPS观测网络中共模分量的相关加权叠加滤波[J],地震学报,2011,33(2):198--208
[6]Savage J.Principal component analysis of in-terseismic deformation in southem Cali-fornia[J].Geophy Res,1995,100.12691-12701
[7]Wu Yanqiang,Huang Liren.A new interpolation method in time series analyzing[J].Journal of geodsy and geodynamics,2004,24(4).武艳强,黄立人.时间序列处理的新插值方法[J].大地测量与地球动力学,2004,24(4)
[8]Rosanne Nikolaidis,Observation of Geodetic and Seismic Deformation with the Global Positioning System[D].San Digeo:University of California,2002
[9]Lai yian,Huang liren,et al.Present-day Crustal movement in China[M].Seismological Press,Beijing,2004.赖锡安,黄立人,等.中国大陆现今地壳运动[M],北京:地震出版社,2004
[10]Liu gengyou.Some Key Issues Relating to High Precision GPS Positioning and Crustal Deformation Analysis[D].Institute of Geodesy and Geophysics,CAS,Wuhan,2004.刘根友.高精度GPS定位及地壳形变分析若干问题的研究[D],武汉:中国科学院测量与地球物理研究所,2004
[11]Rong ming,zhou wei,et al.Global plate motion model based on ITRF2005[J].hydrographic surveying and charting,2009,29(1):1-3.荣敏,周巍等.基于ITRF2005的全球板块运动模型[J],海洋测绘,2009,29(1):1-3