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第十一届中国卫星导航年会

作者： Chunqiao Xie CuilinKuang Jiugang Xie School of Geosciences and Info-Physics Central South University

In order to estimate the velocity results of crustal movement in Southwest China,the coordinate time series of 54 gnss fiducial stations of CMONOC in the area from 2011 to 2018,are selected in this pa

关键词： gnss coordinate time series Station movement velocity Velocity uncertainty MLE MIDAS

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Spatiotemporal filtering of gnss coordinate time series based on variational Bayesian independent component analysis

Spatiotemporal filtering of GNSS coordinate time series base...

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第十三届中国卫星导航年会

作者： Han Gao Cuilin Kuang School of Geoscience and Info-physics Central South University

gnss coordinate time series contain rich constructive and nonconstructive information, and are mixed with noise due to observation conditions and other factors, so denoising and refining them can effectively improve the application level. In this paper, we use variational Bayesian independent component analysis（vbICA） algorithm to filter the gnss coordinate time series of 12 stations in Sichuan and Yunnan regions from 2011 to 2015 to reduce the influence of common mode errors. The experimental results show that after vbICA filtering, root mean square of coordinate residual series in N, E and U directions of the selected 12 stations are reduced by 26.5%, 23.7% and 39.1% on average, respectively. The station velocities were estimated from the coordinate time series before and after filtering, and velocity uncertainties in the N, E, and U directions were reduced by 40.71%, 41.2%, and 52.34% on average, respectively, after filtering. Above data prove that the filtering of coordinate time series by vbICA algorithm is effective. Meanwhile, the filtering results of vbICA algorithm and PCA algorithm are compared, and the former is better than the latter on the whole.

关键词： gnss coordinate time series variational Bayesian Independent Component Analysis spatiotemporal filtering common mode error

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Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

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Geodesy and Geodynamics 2025年第1期16卷 42-54页

作者： Liansheng Deng Yugang Xiao Qusen Chen Wei Peng Zhao Li Hua Chen Zhiwen Wu School of Electrical and Electronic Information Engineering Hubei Polytechnic University Hubei Luojia Laboratory GNSS Research Center Wuhan University Changjiang Spatial Information Technology Engineering Co. Ltd. Key Laboratory of Spatial Data Mining & Information Sharing of the Ministry of Education Fuzhou University School of Geodesy and Geomatics Wuhan University The First Surveying and Mapping Institute of Hunan Province

The time-varying periodic variations in Global Navigation Satellite System(gnss) stations affect the reliable time series analysis and appropriate geophysical interpretation. In this study, we apply the singular spectrum analysis(SSA) method to characterize and interpret the periodic patterns of gnss deformations in China using multiple geodetic datasets. These include 23-year observations from the Crustal Movement Observation Network of China(CMONOC), displacements inferred from the Gravity Recovery and Climate Experiment(GRACE), and loadings derived from Geophysical models(GM). The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations, and the SSA method outperforms the traditional least squares fitting(LSF) method in extracting and interpreting the time-varying seasonal signals from the original time series. The decrease in the root mean square(RMS) correlates well with the annual cycle variance estimated by the SSA method, and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF method. With SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten eigenvalues. Moreover, both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the gnss periodic variations,and the geophysical effects can account for 71% of the gnss annual amplitudes, and the average RMS reduction is 15%. The SSA method has proved to be useful for investigating the gnss timevarying seasonal signals. It could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.

关键词： gnss coordinate time series Singular spectrum analysis time-varying seasonal signals Loading effects GRACE

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