Message Board

Respected readers, authors and reviewers, you can add comments to this page on any questions about the contribution, review,        editing and publication of this journal. We will give you an answer as soon as possible. Thank you for your support!

Name
E-mail
Phone
Title
Content
Verification Code
Turn off MathJax
Article Contents

LIU Xikang, DING Zhifeng, LI Yuan, LIU Zhiguang. Application of EMD in GNSS Time Series Periodic Term Processing[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210029
Citation: LIU Xikang, DING Zhifeng, LI Yuan, LIU Zhiguang. Application of EMD in GNSS Time Series Periodic Term Processing[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210029

Application of EMD in GNSS Time Series Periodic Term Processing

doi: 10.13203/j.whugis20210029
Funds:

The National Key Research and Development Program of China (2018YFC1503606)

  • Received Date: 2021-01-19
  • Global Navigation Satellite System(GNSS) position time series usually includes both tectonic and non-tectonic deformation information, have the characterizes of complex components, difficult modeling, and difficult to effectively separate the non-tectonic signals from original time series. How to remove the non-tectonic deformation information is very important for the accurate and effective application of the observation data. Empirical Mode Decomposition(EMD) is an adaptive time-frequency processing method, we use this method to correct the period term of 24 GNSS continuous station time series in Sichuan and Yunnan areas. The results show that the correction of the periodic term is necessary, EMD method is able to extract the periodic components of different frequencies and amplitudes adaptively according to each station's own characteristics, which is also more in line with the actual situation. The average Root Mean Square error(RMS) is reduced by 19.96%, 11.57% and 38.50% compared to the original time series of N, E and U direction, respectively. It is a more accurate and effective method than harmonic model correction. Then, we use the modified continuous station time series to simulate the mobile observations, found that relatively reliable motion velocities can be obtained after 5~6 years/period of observations. The stability and reliability of the EMD method were verified by correcting the period term of the mobile station with the actual continuous observation station at a closer distance, which also provides a reference and theoretical basis for the implementation of mobile GNSS observations and the correction of observation data.
  • [1] Dong D, Fang P, Bock Y, Chen M K, Miyazaki S. 2002. Anatomy of Apparent Seasonal Variations from GPS-Derived Site Position Time Series. Journal of Geophysical Research, 107:B4.
    [2] Jiang W, Yuan P, Chen H, et al. Annual Variations of Monsoon and Drought Detected by GPS:A Case Study in Yunnan, China[J]. Scientific Reports, 2017, 7(1):5874.
    [3] Davis J L, Wernicke B P, Tamisiea M E. On Seasonal Signals in Geodetic Time Series[J]. Journal of Geophysical Research, 2012, 117(B01403).
    [4] Chen Q, Van Dam T, Sneeuw N, et al. Singular Spectrum Analysis for Modeling Seasonal Signals from GPS Time Series[J]. Journal of Geodynamic, 2013, 72:25-35
    [5] Gruszczynska M, Rosat S, Klos A, et al. Multichannel Singular Spectrum Analysis in the Estimates of Common Environmental Effects Affecting GPS Observations[J]. Pure and Applied Geophysics, 2018,175(5).
    [6] Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-Stationary Time Series Analysis[J]. Proceedings of the Royal Society of London A:Mathematical, Physical and Engineering Sciences, 1998, 454(1971):903-995
    [7] Chen C H, Wen S, Liu J Y, et al. Surface Deformation and Seismic Rebound:Implications and Applications[J]. Surveys in Geophysics, 2011, 32:291-313
    [8] Chen C H, Wen S, Yeh Ta-Kang, et al. Observation of Surface Displacements from GPS Analyses before and after the Jiashian Earthquake (M=6.4) in Taiwan[J]. Journal of Asian Earth Sciences, 2013, 62:662-671
    [9] C. H.Chen, C. H. Wang, S. Wen, Yeh T K, et al. Anomalous Frequency Characteristics of Groundwater Level before Major Earthquake in Taiwan[J]. Hydrology and Earth System Sciences, 2013, 17:1693-1703
    [10] Feng Wei, Ren Jinwei, Jiang Zaisen. GPS Station Short-Term Dynamic Characteristics of Micro Displacement before Menyuan M6.4 Earthquake[J]. Geodesy and Geodynamics, 2016, 7(4):237-244
    [11] Herring T A, King R W, McClusky S C. GAMIT Reference Manual:GPS Analysis at MIT, Version 10.4[R]. Massachusetts Institute of Technology, Cambridge, 2010
    [12] Van Dam, T. M., Wahr, J., Lavallée, D. A comparison of annualvertical crustal displacements from GPS and Gravity Recovery and Climate Experiment (GRACE) over Europe[J]. Journal of Geophysical Research:Solid Earth, 2007, 112, B03404.
    [13] Zhan Wei, Li Fei, Hao Weifeng, et al. Regional Characteristics and Influencing Factors of Seasonal Vertical Crustal Motions in Yunnan, China[J]. Geophysical Journal International, 2017, 210(3):1295-1304
    [14] Liang Hongbao, Zhan Wei, Li Jinwu. Vertical Surface Displacement of Mainland China from GPS Using the Multisurface Function Method[J]. Advances in Space Research, https://doi.org/10.1016/j.asr.2021.02.024
  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Article Metrics

Article views(288) PDF downloads(23) Cited by()

Related
Proportional views

Application of EMD in GNSS Time Series Periodic Term Processing

doi: 10.13203/j.whugis20210029
Funds:

The National Key Research and Development Program of China (2018YFC1503606)

Abstract: Global Navigation Satellite System(GNSS) position time series usually includes both tectonic and non-tectonic deformation information, have the characterizes of complex components, difficult modeling, and difficult to effectively separate the non-tectonic signals from original time series. How to remove the non-tectonic deformation information is very important for the accurate and effective application of the observation data. Empirical Mode Decomposition(EMD) is an adaptive time-frequency processing method, we use this method to correct the period term of 24 GNSS continuous station time series in Sichuan and Yunnan areas. The results show that the correction of the periodic term is necessary, EMD method is able to extract the periodic components of different frequencies and amplitudes adaptively according to each station's own characteristics, which is also more in line with the actual situation. The average Root Mean Square error(RMS) is reduced by 19.96%, 11.57% and 38.50% compared to the original time series of N, E and U direction, respectively. It is a more accurate and effective method than harmonic model correction. Then, we use the modified continuous station time series to simulate the mobile observations, found that relatively reliable motion velocities can be obtained after 5~6 years/period of observations. The stability and reliability of the EMD method were verified by correcting the period term of the mobile station with the actual continuous observation station at a closer distance, which also provides a reference and theoretical basis for the implementation of mobile GNSS observations and the correction of observation data.

LIU Xikang, DING Zhifeng, LI Yuan, LIU Zhiguang. Application of EMD in GNSS Time Series Periodic Term Processing[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210029
Citation: LIU Xikang, DING Zhifeng, LI Yuan, LIU Zhiguang. Application of EMD in GNSS Time Series Periodic Term Processing[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210029
Reference (14)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return