Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis
GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement nois...
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| Format: | Article |
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Springer
2015
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| Online Access: | https://eprints.nottingham.ac.uk/35372/ |
| _version_ | 1848795062452879360 |
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| author | Liu, Bin Dai, Wujiao Peng, Wei Meng, Xiaolin |
| author_facet | Liu, Bin Dai, Wujiao Peng, Wei Meng, Xiaolin |
| author_sort | Liu, Bin |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis. |
| first_indexed | 2025-11-14T19:26:07Z |
| format | Article |
| id | nottingham-35372 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:26:07Z |
| publishDate | 2015 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-353722020-05-04T17:21:39Z https://eprints.nottingham.ac.uk/35372/ Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis Liu, Bin Dai, Wujiao Peng, Wei Meng, Xiaolin GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis. Springer 2015-11-25 Article PeerReviewed Liu, Bin, Dai, Wujiao, Peng, Wei and Meng, Xiaolin (2015) Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis. Earth, Planets and Space, 67 . ISSN 1880-5981 Vertical GPS time series; Non-tectonic deformation; Spatiotemporal analysis; Common-mode error; Independent component analysis http://earth-planets-space.springeropen.com/articles/10.1186/s40623-015-0357-1 doi:10.1186/s40623-015-0357-1 doi:10.1186/s40623-015-0357-1 |
| spellingShingle | Vertical GPS time series; Non-tectonic deformation; Spatiotemporal analysis; Common-mode error; Independent component analysis Liu, Bin Dai, Wujiao Peng, Wei Meng, Xiaolin Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title | Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title_full | Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title_fullStr | Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title_full_unstemmed | Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title_short | Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis |
| title_sort | spatiotemporal analysis of gps time series in vertical direction using independent component analysis |
| topic | Vertical GPS time series; Non-tectonic deformation; Spatiotemporal analysis; Common-mode error; Independent component analysis |
| url | https://eprints.nottingham.ac.uk/35372/ https://eprints.nottingham.ac.uk/35372/ https://eprints.nottingham.ac.uk/35372/ |