Singular spectrum analysis for modeling seasonal signals from GPS time series

authored by: Q. Chen, T. van Dam, N. Sneeuw, X. Collilieux, M. Weigelt, P. Rebischung
Abstract: 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.
External Organisation(s): University of Stuttgart
University of Luxembourg
Université de Paris
Type: Article
Journal: Journal of geodynamics
Volume: 72
Pages: 25-35
No. of pages: 11
ISSN: 0264-3707
Publication date: 12.2013
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Geophysics, Earth-Surface Processes
Electronic version(s): https://doi.org/10.1016/j.jog.2013.05.005 (Access: Unknown)
: Details in the research portal "Research@Leibniz University"

BibTeX

@article{4d59583840a44b2a8ba4a0bb320cc60c,
title = "Singular spectrum analysis for modeling seasonal signals from GPS time series",
abstract = "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.",
keywords = "GPS time series, Kalman filtering, Least-squares fitting, Modulated seasonal signals, Singular spectrum analysis",
author = "Q. Chen and {van Dam}, T. and N. Sneeuw and X. Collilieux and M. Weigelt and P. Rebischung",
note = "Funding Information: We would like to thank two anonymous reviewers for helpful comments and suggestions on the manuscript. Q. Chen is supported by China Scholarship Council (CSC) for his PhD study.",
year = "2013",
month = dec,
doi = "10.1016/j.jog.2013.05.005",
language = "English",
volume = "72",
pages = "25--35",
journal = "Journal of geodynamics",
issn = "0264-3707",
publisher = "Elsevier Ltd.",
}