Gravity Tides Extracted from Relative Gravimeter Data by Combining Empirical Mode Decomposition and Independent Component Analysis

  • Hongjuan Yu
  • Jinyun Guo
  • Qiaoli Kong
  • Xiaodong Chen


The static observation data from a relative gravimeter contain noise and signals such as gravity tides. This paper focuses on the extraction of the gravity tides from the static relative gravimeter data for the first time applying the combined method of empirical mode decomposition (EMD) and independent component analysis (ICA), called the EMD-ICA method. The experimental results from the CG-5 gravimeter (SCINTREX Limited Ontario Canada) data show that the gravity tides time series derived by EMD-ICA are consistent with the theoretical reference (Longman formula) and the RMS of their differences only reaches 4.4 μGal. The time series of the gravity tides derived by EMD-ICA have a strong correlation with the theoretical time series and the correlation coefficient is greater than 0.997. The accuracy of the gravity tides estimated by EMD-ICA is comparable to the theoretical model and is slightly higher than that of independent component analysis (ICA). EMD-ICA could overcome the limitation of ICA having to process multiple observations and slightly improve the extraction accuracy and reliability of gravity tides from relative gravimeter data compared to that estimated with ICA.


Gravity tides relative gravimeter data CG-5 gravimeter empirical mode decomposition independent component analysis EMD-ICA 



The authors are grateful to the editors and anonymous reviewers for their helpful comments, which led a significant improvement in this paper. This study is partially supported by the National Natural Science Foundation of China (Grant No. 41774001, 41374009 & 41574072), the Special Project of Basic Science and Technology of China (Grant No. 2015FY310200), the Shandong Natural Science Foundation of China (Grant No. ZR2013DM009), and the SDUST Research Fund (Grant No. 2014TDJH101).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Geodesy and GeomaticsShandong University of Science and TechnologyQingdaoPeople’s Republic of China
  2. 2.College of Surveying and Geo-informaticsTongji UniversityShanghaiPeople’s Republic of China
  3. 3.State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and Ministry of Science & TechnologyShandong University of Science and TechnologyQingdaoPeople’s Republic of China
  4. 4.State Key Laboratory of Geodesy and Earth’s DynamicsInstitute of Geodesy and Geophysics, Chinese Academy of SciencesWuhanPeople’s Republic of China

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