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Time-Varying Analysis and Prediction of Geocentric Motion
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Erhu WEI1, Junjie WU1, Yunlong ZHANG2, Yile LUO1, Xiancai ZOU1, Xiaojing TIAN3, Jingnan LIU4
Journal of Geodesy and Geodynamics | 2025, 45(4) : 331 - 338
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Journal of Geodesy and Geodynamics | 2025, 45(4): 331-338
Time-Varying Analysis and Prediction of Geocentric Motion
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Erhu WEI1, Junjie WU1, Yunlong ZHANG2, Yile LUO1, Xiancai ZOU1, Xiaojing TIAN3, Jingnan LIU4
Affiliations
  • 1 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
  • 2 China Railway Design Corporation, 10 Minjiang Road, Tianjin 300251, China
  • 3 Geodetic Data Processing Center, MNR, 334 East-Youyi Road, Xi'an 710054, China
  • 4 GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
  • WEI Erhu, PhD, professor, PhD supervisor, majors in space geodesy and navigation, E-mail:

About Author:

WEI Erhu, PhD, professor, PhD supervisor, majors in space geodesy and navigation, E-mail:

Published: 2025-04-15 doi: 10.14075/j.jgg.2024.07.317
Outline
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This paper uses the time series of the geocentric motion provided by the Center for Space Research at the University of Texas at Austin as experimental data. First, empirical mode decomposition (EMD) is applied to the data for noise reduction. Then, fast Fourier transform (FFT) and continuous wavelet transform (CWT) are utilized to perform frequency domain transformation, power spectrum analysis, and extraction of periodic terms. Finally, an autoregressive integrated moving average (ARIMA) model and exponential smoothing method are employed to forecast the geocentric motion for the next 20 months. The results indicate that the amplitude and phase of the annual term extracted using FFT are quite close to previous studies on the geocentric motion. The ARIMA model provides better predictions for the Y-direction motion of the geocentric motion over the next 20 months, while exponential smoothing method offers more accurate predictions for the X and Z directions.

geocentric motion  /  fast Fourier transform  /  continuous wavelet transform  /  ARIMA  /  exponential smoothing method
魏二虎, 吴俊杰, 张云龙, 罗一乐, 邹贤才, 田晓静, 刘经南. 地心运动的时变分析与预报. 大地测量与地球动力学, 2025 , 45 (4) : 331 -338 . DOI: 10.14075/j.jgg.2024.07.317
Erhu WEI, Junjie WU, Yunlong ZHANG, Yile LUO, Xiancai ZOU, Xiaojing TIAN, Jingnan LIU. Time-Varying Analysis and Prediction of Geocentric Motion[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (4) : 331 -338 . DOI: 10.14075/j.jgg.2024.07.317
  • National Natural Science Foundation of China(42374015)
  • National Key Research and Development Program of China(2018YFC1503600)
  • Open Fund of Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-Temporal Big Data Technology(TKL2024B04)
Year 2025 volume 45 Issue 4
20
8
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Article Info
doi: 10.14075/j.jgg.2024.07.317
  • Receive Date:2024-07-05
  • Online Date:2026-10-30
  • Published:2025-04-15
Article Data
Affiliations
History
  • Received:2024-07-05
Funding
National Natural Science Foundation of China(42374015)
National Key Research and Development Program of China(2018YFC1503600)
Open Fund of Tianjin Key Laboratory of Rail Transit Navigation Positioning and Spatio-Temporal Big Data Technology(TKL2024B04)
Affiliations
    1 School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
    2 China Railway Design Corporation, 10 Minjiang Road, Tianjin 300251, China
    3 Geodetic Data Processing Center, MNR, 334 East-Youyi Road, Xi'an 710054, China
    4 GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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