Reference : Singular spectrum analysis for modeling seasonal signals from GPS time series

	Document type :	Scientific journals : Article
	Discipline(s) :	Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography Engineering, computing & technology : Computer science Engineering, computing & technology : Multidisciplinary, general & others
	To cite this reference:	http://hdl.handle.net/10993/3271

Title :	Singular spectrum analysis for modeling seasonal signals from GPS time series
Language :	English
Author, co-author :	Chen, Qiang [University of Stuttgart > Institute of Geodesy]
	van Dam, Tonie [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
	Sneeuw, Nico [University of Stuttgart > Institute of Geodesy]
	Collilieux, Xavier [Université Paris Diderot - Paris 7 > IGN/LAREG]
	Weigelt, Matthias [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
	Rebischung, Paul [Université Paris Diderot - Paris 7 > IGN/LAREG]
Publication date :	Dec-2013
Journal title :	Journal of Geodynamics
Publisher :	Elsevier Science
Volume :	72
Pages :	25-35
Peer reviewed :	Yes (verified by ORBilu)
Audience :	International
ISSN :	0264-3707
City :	Oxford
Country :	United Kingdom
Keywords :	[en] GPS time series ; Kalman filtering ; least-squares fitting ; Modulated seasonal signals ; singular spectrum analysis
Abstract :	[en] 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.
Permalink :	http://hdl.handle.net/10993/3271
DOI :	10.1016/j.jog.2013.05.005
Mentions required by the publisher for OA :	NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Geodynamics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Geodynamics, [in press] DOI#10.1016/j.jog.2013.05.005

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