Home Journals Articles Updates About
CN
image
收藏切换
On Accuracy of Time-Varying Gravity Field Inversion Based on Optimized Preprocessing Method
收藏切换
Lun PU1, 2, Wei YOU1, *, Biao YU1, Dongming FAN1
Journal of Geodesy and Geodynamics | 2025, 45(1) : 72 - 79
Less
收藏切换
Journal of Geodesy and Geodynamics | 2025, 45(1): 72-79
On Accuracy of Time-Varying Gravity Field Inversion Based on Optimized Preprocessing Method
Full
Lun PU1, 2, Wei YOU1, *, Biao YU1, Dongming FAN1
Affiliations
  • 1 Faculty of Geosciences and Engineering, Southwest Jiaotong University, 999 Xi'an Road, Chengdu 611756, China
  • 2 Global Geomonitoring and Gravity Field, GFZ German Research Centre for Geosciences, 1 Claude-Dornier-Straße, Wessling 82234, Germany

    • PU Lun, PhD candidate, majors in satellite gravity measurement data processing and application, E-mail:

About Author:

PU Lun, PhD candidate, majors in satellite gravity measurement data processing and application, E-mail:

Published: 2025-01-15 doi: 10.14075/j.jgg.2024.03.097
Outline
收藏切换

Aiming at the problem of missing data and gross errors in GRACE Level1B observations, we propose an optimization method to complete the missing data of SCA1B. At the same time, we use an optimization strategy to eliminate gross errors for KBR1B and kinematic orbit data, then use these data sets to invert the time-varying gravity field model. In addition, based on synthesized data and real data respectively, we analyze the impact of the error from ACC1B in the Y direction on the inversion results and propose an optimization correction strategy. The optimized completion method can recover the missing data of SCA1B effectively, and fully considers the variational characteristics of the observations throughout the arc. The accuracy of the result derived from ACC1B data after correction using the optimization strategy is 3.7 mm, which is higher than the result derived from the uncorrected data. It can be seen from the results that the overall accuracy of the gravity field model solved by the data processed using the optimization method is like that of the three official centers. However, the detailed signals performance of the results from different institutions in regionals are different. It shows that the optimized data preprocessing strategy is effective and feasible.

GRACE  /  data preprocessing  /  time-varying gravity field  /  time series decomposition  /  accuracy analysis
蒲伦, 游为, 余彪, 范东明. 基于优化预处理方法的时变重力场反演精度分析. 大地测量与地球动力学, 2025 , 45 (1) : 72 -79 . DOI: 10.14075/j.jgg.2024.03.097
Lun PU, Wei YOU, Biao YU, Dongming FAN. On Accuracy of Time-Varying Gravity Field Inversion Based on Optimized Preprocessing Method[J]. Journal of Geodesy and Geodynamics, 2025 , 45 (1) : 72 -79 . DOI: 10.14075/j.jgg.2024.03.097
Funding
Less
  • National Natural Science Foundation of China(41974013)
  • National Natural Science Foundation of China(42174014)
  • National Natural Science Foundation of China(42474005)
Appendix
Less
Year 2025 volume 45 Issue 1
26
10
Cite this Article
BibTeX
Article Info
doi: 10.14075/j.jgg.2024.03.097
  • Receive Date：2024-03-08
  • Online Date：2026-10-30
  • Published：2025-01-15
Article Data
Affiliations
History
  • Received：2024-03-08
Funding
National Natural Science Foundation of China(41974013)
National Natural Science Foundation of China(42174014)
National Natural Science Foundation of China(42474005)
Affiliations
    1 Faculty of Geosciences and Engineering, Southwest Jiaotong University, 999 Xi'an Road, Chengdu 611756, China
    2 Global Geomonitoring and Gravity Field, GFZ German Research Centre for Geosciences, 1 Claude-Dornier-Straße, Wessling 82234, Germany
References
Share
https://castjournals.cast.org.cn/joweb/ddcl/EN/10.14075/j.jgg.2024.03.097
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
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
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House