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基于奇异谱分析的CORS大地高时序形变信息提取
引用本文:李婉秋,王伟,章传银,钟玉龙,尹财,朱钰.基于奇异谱分析的CORS大地高时序形变信息提取[J].大地测量与地球动力学,2019,39(8):825-831.
作者姓名:李婉秋  王伟  章传银  钟玉龙  尹财  朱钰
作者单位:中国测绘科学研究院,北京市莲花池西路28号,100830;山东科技大学测绘科学与工程学院,青岛市前湾港路579号,266590;中国测绘科学研究院,北京市莲花池西路28号,100830;中国地质大学(武汉)信息工程学院,武汉市鲁磨路388号,430074;青岛市勘察测绘研究院,青岛市山东路189号,266032;云南大学国际河流与生态安全研究院,昆明市云学路,650500
基金项目:国家自然科学基金;国家自然科学基金;国家重点研发计划;中国测绘科学研究院基本科研业务费专项;中央级公益性科研院所基本科研业务费专项
摘    要:为增强信号提取的信噪比,采用奇异谱分析(SSA)对测站大地高序列进行分解与重构,利用仿真实验确定嵌入维数与截断主分量个数的选取原则,进一步将SSA方法应用到CORS时序形变信息提取中。通过FFT分析发现,大地高时序以分解的季节性变化成分为主,重构后时序标准差明显减小,表明SSA方法可有效提高CORS大地高时序信噪比,起到降噪平滑作用。研究结果对地壳垂直形变特征表达及SSA应用拓展具有一定意义。

关 键 词:CORS  大地高时序  信号提取  奇异谱分析  信噪比

Deformation Signal Extraction of Geodetic Height Change Time Series of CORS Based on Singular Spectrum Analysis
LI Wanqiu,WANG Wei,ZHANG Chuanyin,ZHONG Yulong,YIN Cai,ZHU Yu.Deformation Signal Extraction of Geodetic Height Change Time Series of CORS Based on Singular Spectrum Analysis[J].Journal of Geodesy and Geodynamics,2019,39(8):825-831.
Authors:LI Wanqiu  WANG Wei  ZHANG Chuanyin  ZHONG Yulong  YIN Cai  ZHU Yu
Abstract:In order to enhance the signal-to-noise ratio (SNR) of signal extraction, the singular spectrum analysis (SSA) method is used to decompose and reconstruct the geodesic height time series, using the simulation experiment to determine the principle of selecting the embedding dimension and the number of truncated principal components. Furthermore, the SSA method is applied to the extraction of deformation information of CORS. By FFT analysis, we find that the geodetic height time series mainly consists of decomposed seasonal variation components, and the standard deviation of time series after reconstruction is significantly reduced. Our research shows that the SSA method can effectively improve the geodetic height time series SNR of CORS and play a role in noise reduction and smoothing. The research results have certain significance for the expression of vertical deformation of the crust and the expansion of SSA applications.
Keywords:CORS  geodetic height time series  signal extraction  SSA  SNR  
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