GNSS Vertical Coordinate Time Series Analysis Using Single-Channel Independent Component Analysis Method
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- 作者：Wei Peng ; Wujiao Dai ; Rock Santerre ; Changsheng Cai…
- 关键词：Phase space reconstruction ; based independent component analysis ; ensemble empirical mode decomposition ; mass loading ; hurst parameter ; GNSS vertical coordinate time series
- 刊名：Pure and Applied Geophysics
- 出版年：2017
- 出版时间：February 2017
- 年：2017
- 卷：174
- 期：2
- 页码：723-736
- 全文大小：
- 刊物类别：Earth and Environmental Science
- 刊物主题：Geophysics/Geodesy;
- 出版者：Springer International Publishing
- ISSN：1420-9136
- 卷排序：174
文摘
Daily vertical coordinate time series of Global Navigation Satellite System (GNSS) stations usually contains tectonic and non-tectonic deformation signals, residual atmospheric delay signals, measurement noise, etc. In geophysical studies, it is very important to separate various geophysical signals from the GNSS time series to truthfully reflect the effect of mass loadings on crustal deformation. Based on the independence of mass loadings, we combine the Ensemble Empirical Mode Decomposition (EEMD) with the Phase Space Reconstruction-based Independent Component Analysis (PSR-ICA) method to analyze the vertical time series of GNSS reference stations. In the simulation experiment, the seasonal non-tectonic signal is simulated by the sum of the correction of atmospheric mass loading and soil moisture mass loading. The simulated seasonal non-tectonic signal can be separated into two independent signals using the PSR-ICA method, which strongly correlated with atmospheric mass loading and soil moisture mass loading, respectively. Likewise, in the analysis of the vertical time series of GNSS reference stations of Crustal Movement Observation Network of China (CMONOC), similar results have been obtained using the combined EEMD and PSR-ICA method. All these results indicate that the EEMD and PSR-ICA method can effectively separate the independent atmospheric and soil moisture mass loading signals and illustrate the significant cause of the seasonal variation of GNSS vertical time series in the mainland of China.