Details
| Original language | English |
|---|---|
| Pages (from-to) | 25-35 |
| Number of pages | 11 |
| Journal | Journal of geodynamics |
| Volume | 72 |
| Publication status | Published - Dec 2013 |
| Externally published | Yes |
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
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)
- Geophysics
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
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In: Journal of geodynamics, Vol. 72, 12.2013, p. 25-35.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
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.
PY - 2013/12
Y1 - 2013/12
N2 - 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.
AB - 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.
KW - GPS time series
KW - Kalman filtering
KW - Least-squares fitting
KW - Modulated seasonal signals
KW - Singular spectrum analysis
UR - http://www.scopus.com/inward/record.url?scp=84888833909&partnerID=8YFLogxK
U2 - 10.1016/j.jog.2013.05.005
DO - 10.1016/j.jog.2013.05.005
M3 - Article
AN - SCOPUS:84888833909
VL - 72
SP - 25
EP - 35
JO - Journal of geodynamics
JF - Journal of geodynamics
SN - 0264-3707
ER -