基于EMD方法的地心运动时间序列分析
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摘要
针对时间序列混有的高频信息会影响地心运动规律分析的问题,采用网平移法对IGS提供的GNSS周解进行解算,得到2007—2017年地心运动时间序列,对其进行分解重构,剔除高频项,并利用重构时序对地心运动规律作进一步分析探讨。结果表明:本文解算的地心运动在Tx、Ty和Tz方向的精度均为毫米级。EMD方法重构的时序保留了原序列的基本信息,且抑制了高频项的影响,提高了周期贡献率,3个方向的贡献率分别提高了12.3%、16.7%及6.3%。通过分析重构后的时序发现,周年项振幅为各周期对应振幅的最大值,分别为2.32、1.89和2.07 mm;Ty和Tz方向长期变化趋势较Tx更为明显,分别为0.13和-0.27 mm/a;半年项较小,且在Tx和Ty方向上具有时变性。此外,还发现了一些其他较小的年际变化。
The high-frequency information mixed with geocenter motion time series will affect the analysis of the geocenter motion.This paper uses the EMD method to decompose and reconstruct the geocenter motion time series from 2007 to 2017 calculated by translation approach using the GNSS solution provided by the IGS,then removes high-frequency terms,and performs the further analysis using the reconstructed geocenter motion time series.The results show that the geocenter motion calculated in the Tx,Ty,and Tzdirections are at the millimeter level.The reconstructed time series using EMD method preserves the basic information of the original time series,weakens the influence of high-frequency terms,and improves the contribution rate of each periods,contribution rate in three directions increase by 12.3%,16.7% and 6.3% respectively.By analyzing the reconstructed time series,the annual amplitudes in three directions are maximum,compared to other period,which are 2.32,1.89,and 2.07 mm,respectively.In addition,the long-term trends in the Ty and Tzdirections are more pronounced than those of Tx,which are 0.13 and-0.27 mm/a,respectively.The semi-annual term is weak and has a time variant features in the Txand Tydirections.Moreover,some other minor interannual variations have also been discovered.
The high-frequency information mixed with geocenter motion time series will affect the analysis of the geocenter motion.This paper uses the EMD method to decompose and reconstruct the geocenter motion time series from 2007 to 2017 calculated by translation approach using the GNSS solution provided by the IGS,then removes high-frequency terms,and performs the further analysis using the reconstructed geocenter motion time series.The results show that the geocenter motion calculated in the Tx,Ty,and Tzdirections are at the millimeter level.The reconstructed time series using EMD method preserves the basic information of the original time series,weakens the influence of high-frequency terms,and improves the contribution rate of each periods,contribution rate in three directions increase by 12.3%,16.7% and 6.3% respectively.By analyzing the reconstructed time series,the annual amplitudes in three directions are maximum,compared to other period,which are 2.32,1.89,and 2.07 mm,respectively.In addition,the long-term trends in the Ty and Tzdirections are more pronounced than those of Tx,which are 0.13 and-0.27 mm/a,respectively.The semi-annual term is weak and has a time variant features in the Txand Tydirections.Moreover,some other minor interannual variations have also been discovered.
引文
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[2]RIETBROEK R,FRITSCHE M,BRUNNABEND S E,et al.Global surface mass from a new combination of GRACE,modelled OBP and reprocessed GPS data[J].Journal of Geodynamics,2012,59-60(5):64-71.
[3]ALTAMIMI Z,COLLILIEUX X,METIVIER L.ITRF2008:an improved solution of the international terrestrial reference frame[J].Journal of Geodesy,2011,85(8):457-473.
[4]杜彬,杨强,章传银,等.GPS网平移法反演地心运动研究[J].导航定位学报,2013,1(2):38-41.
[5]郭金运.由SLR观测的地心周期性运动1993-2006[J].测绘学报,2009,38(4):311-317.
[6]郭金运,韩延本,黄金维.由CHAMP卫星监测的地球质心运动分析[J].中国科学(G辑),2008(10):1404-1413.
[7]秦显平,杨元喜.用SLR数据导出的地心运动结果[J].测绘学报,2003,32(2):120-124.
[8]杨强,杜彬,党亚民,等.利用SLR反演地心运动[J].导航定位学报,2013,1(2):45-48.
[9]魏娜,施闯,刘经南.利用GPS数据反演地心运动[J].武汉大学学报(信息科学版),2011,36(4):441-445.
[10]LAVALLE D A,DAM T V,BLEWITT G,et al.Geocenter motions from GPS:an unified observation model[J].Journal of Geophysical Research Solid Earth,2006,111:B05405.
[11]贾瑞生,赵同彬,孙红梅,等.基于经验模态分解及独立成分分析的微震信号降噪方法[J].地球物理学报,2015,58(3):1013-1023.
[12]PAN Y,SHEN W B,DING H,et al.The quasi-biennial vertical oscillations at global GPS stations:identification by ensemble empirical mode decomposition[J].Sensors,2015,15(10):26096-26114.
[13]张双成,何月帆,李振宇,等.EMD用于GPS时间序列降噪分析[J].大地测量与地球动力学,2017,37(12):1248-1252.
[14]王笑蕾,张勤,张双成.基于EMD和WD联合算法的GPS水汽时间序列的周期性振荡分析[J].武汉大学学报(信息科学版),2018,43(4):620-628.
[15]魏娜,施闯,耿长江,等.参考框架的HELMERT转换[J].大地测量与地球动力学,2010,30(4):109-112.
[16]吴斌,彭碧波,许厚泽.地心变化的测定[J].科学通报,1999,44(10):1106-1108.
[17]DEVOTI R,FERMI M,SCIARRETTA C,et al.CGSgeocenter time series from SLR and GPS data[J].Iers Technical Note,1999,25:41-46.
[18]HUANG E.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time seris[M].[S.l.]:The Royal Society,1998.
[19]赵德军,李潭欣,李婧,等.DORIS、GPS和SLR空间大地测量技术导出的地心运动规律[J].测绘工程,2015,24(12):21-24.
[20]郭海荣,杨元喜,焦文海.地心运动时间序列的抗差谱分析[J].测绘学报,2003,32(4):308-312.
[21]METIVIER L,LEFFTZ M G,ALTAMIMI Z.On secular geocenter motion:the impact of climate changes[J].Earth&Planetary Science Letters,2010,296(3):360-366.