Singular spectrum analysis for modeling seasonal signals from GPS time series

verfasst von
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.

Externe Organisation(en)
Universität Stuttgart
University of Luxembourg
Université de Paris
Typ
Artikel
Journal
Journal of geodynamics
Band
72
Seiten
25-35
Anzahl der Seiten
11
ISSN
0264-3707
Publikationsdatum
12.2013
Publikationsstatus
Veröffentlicht
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Geophysik, Erdoberflächenprozesse
Elektronische Version(en)
https://doi.org/10.1016/j.jog.2013.05.005 (Zugang: Unbekannt)
 

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