期刊
JOURNAL OF GEODYNAMICS
卷 72, 期 -, 页码 25-35出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jog.2013.05.005
关键词
GPS time series; Modulated seasonal signals; Singular spectrum analysis; Least-squares fitting; Kalman filtering
资金
- China Scholarship Council (CSC)
Seasonal signals in GPS time series are of great importance for understanding the evolution of regional mass fluctuations, i.e., ice, hydrology, and ocean mass. Conventionally these signals (quasi-annual and semi-annual signals) are modeled by least-squares fitting harmonic terms with a constant amplitude and phase. In reality, however, such seasonal signals are modulated, i.e., they will have a time-variable amplitude and phase. Recently, Davis et al. (2012) proposed a Kalman filter based approach to capture the stochastic seasonal behavior of geodetic time series. Singular Spectrum Analysis (SSA) is a non-parametric method, which uses time domain data to extract information from short and noisy time series without a priori knowledge of the dynamics affecting the time series. A prominent benefit is that trends obtained in this way are not necessarily linear. Further, true oscillations can be amplitude and phase modulated. In this work, we will assess the value of SSA for extracting time-variable seasonal signals from GPS time series. We compare our SSA-based results to those obtained using (1) least-squares analysis and (2) Kalman filtering. Our results demonstrate that SSA is a viable and complementary tool for extracting modulated oscillations from GPS time series. (C) 2013 Elsevier Ltd. All rights reserved.
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