A continuous GPS coordinate time series analysis strategy for high-accuracy vertical land movements
By Felix Norman Teferle, Simon D. P. Williams, H. Kierulf, Richard M. Bingley, Hans-Peter Plag. 2008
54 citations recorded by the source corpus.
CGPS coordinate time series analysis strategy was evaluated to determine highly accurate vertical station velocity estimates with realistic uncertainties. This strategy uses a combination of techniques to (1) obtain the most accurate parameter estimates of the station motion model, (2) infer the stochastic properties of the time series in order to compute more realistic error bounds for all parameter estimates, and (3) improve the understanding of apparent common systematic variations in the CGPS coordinate time series, which are believed to be of geophysical and/or technical origin. The strategy provided a pre-processing of the coordinate time series in which outliers and discontinuities were identified. Subsequent parameterization included a mean value, a constant rate, periodic terms with annual and semi-annual frequencies, and offset magnitudes for identified discontinuities. All parameters plus the magnitudes of different stochastic noise were determined using maximum likelihood estimation (MLE). Empirical orthogonal function (EOF) analysis was used to study both the temporal and spatial variability of the common modes determined by this technique. After outlining the CGPS coordinate time series analysis strategy this paper shows initial results for coordinate time series for a four year (2000–2003) period from a selection of CGPS stations in Europe that are part of the European Sea Level Service (ESEAS) CGPS network
Subfields: Aerospace Engineering, Astronomy and Astrophysics, Oceanography
View Paper: https://www.semanticscholar.org/paper/aed48afa9b80e82074ca5a1c368844893c742193
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Citations in the corpus
- Noise in multivariate GPS position time-series (2009, background): “Several studies have recognized flicker noise (power-law noise with spectral index κ = −1) in addition to white noise in geodetic time-series (Zhang et al. 1997; Mao et al. 1999; Calais 1999; Williams et al. 2004; Nikolaidis et al. 2001; Teferle et al. 2008; Amiri-Simkooei et al. 2007).”