Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis

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• 作者：Bin Liu ; Wujiao Dai ; Wei Peng ; Xiaolin Meng
• 关键词：Vertical GPS time series ; Non ; tectonic deformation ; Spatiotemporal analysis ; Common ; mode error ; Independent component analysis
• 刊名：Earth, Planets and Space
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：67
• 期：1
• 全文大小：3,951 KB
• 刊物类别：Earth Sciences, general; Geology; Geophysics/Geodesy;
• 刊物主题：Earth Sciences, general; Geology; Geophysics/Geodesy;
• 出版者：Springer Berlin Heidelberg
• ISSN：1880-5981

文摘

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.