Singular spectrum analysis for modeling seasonal signals from GPS time series

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Autorschaft

  • Q. Chen
  • T. van Dam
  • N. Sneeuw
  • X. Collilieux
  • M. Weigelt
  • P. Rebischung

Externe Organisationen

  • Universität Stuttgart
  • University of Luxembourg
  • Université de Paris
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)25-35
Seitenumfang11
FachzeitschriftJournal of geodynamics
Jahrgang72
PublikationsstatusVeröffentlicht - Dez. 2013
Extern publiziertJa

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.

ASJC Scopus Sachgebiete

Zitieren

Singular spectrum analysis for modeling seasonal signals from GPS time series. / Chen, Q.; van Dam, T.; Sneeuw, N. et al.
in: Journal of geodynamics, Jahrgang 72, 12.2013, S. 25-35.

Publikation: Beitrag in FachzeitschriftArtikelForschungPeer-Review

Chen Q, van Dam T, Sneeuw N, Collilieux X, Weigelt M, Rebischung P. Singular spectrum analysis for modeling seasonal signals from GPS time series. Journal of geodynamics. 2013 Dez;72:25-35. doi: 10.1016/j.jog.2013.05.005
Chen, Q. ; van Dam, T. ; Sneeuw, N. et al. / Singular spectrum analysis for modeling seasonal signals from GPS time series. in: Journal of geodynamics. 2013 ; Jahrgang 72. S. 25-35.
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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.",
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T1 - Singular spectrum analysis for modeling seasonal signals from GPS time series

AU - Chen, Q.

AU - van Dam, T.

AU - Sneeuw, N.

AU - Collilieux, X.

AU - Weigelt, M.

AU - Rebischung, P.

N1 - 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.

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