HR: 10:20h
AN: G41C-01 INVITED     [PDF]
TI: Error Analysis of Continuous GPS Height Time Series
AU: * Williams, S D
EM: sdwil@pol.ac.uk
AF: Proudman Oceanographic Laboratory, Joseph Proudman Building 6 Brownlow Street, Liverpool, L3 5DA United Kingdom
AU: Bock, Y
EM: ybock@ucsd.edu
AF: Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics,, Scripps Institution of Oceanography, La Jolla, CA 92093 0225 United States
AU: Fang, P
EM: pfang@ucsd.edu
AF: Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics,, Scripps Institution of Oceanography, La Jolla, CA 92093 0225 United States
AU: Jamason, P
EM: pjamason@ucsd.edu
AF: Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics,, Scripps Institution of Oceanography, La Jolla, CA 92093 0225 United States
AU: Nikolaidis, R M
EM: niko@ucsd.edu
AF: Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics,, Scripps Institution of Oceanography, La Jolla, CA 92093 0225 United States
AU: Prawirodirdjo, L
EM: linette@ucsd.edu
AF: Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics,, Scripps Institution of Oceanography, La Jolla, CA 92093 0225 United States
AB: Until recently, it was typically assumed that geodetic time series, in the absence of earthquakes, consisted of a linear trend representing crustal motion, and measurement errors which were assumed to be normal (Gaussian) and statistically uncorrelated from one another (white noise). However, many geodetic data sets have now provided evidence for signals and noise that introduce large temporal correlations into the data. One common statistical model for many types of geophysical signal may be described as a power-law process, or one with time-domain behavior that has a power-spectrum of the form $P \propto f^{\alpha}$. The spectral index, $\alpha$, is typically in the range$-2 < \alpha < 0$. More than 900 continuous GPS height times series from over 400 individual sites in 9 different GPS solutions were analyzed for power-law noise content using Maximum Likelihood Estimation (MLE). The lengths of the series varied from 500 to 3722 daily position estimates (around 16 months to over 10 years). In solutions where the sites were globally distributed the noise can be best described by a combination of white noise plus flicker noise. Both noise components show latitude dependence in their amplitudes (higher at equatorial sites) together with a bias to lower values in the northern Hemisphere. In the regional solutions, where an attempt has been made to remove a spatially correlated (common-mode) signal, the noise is significantly lower. The spectral index of the power law in regional solutions is more varied than in the global solutions and probably reflects a mixture of origins such as monument instability, hydrological effects, atmospheric effects, and residual common-mode noise. A significant reduction in noise can be seen for all solutions since the first continuous GPS networks began recording in the early 1990s. Until such time that all unwanted signals in the GPS height time series can be removed, a suitable stochastic model can serve as a useful description of the time series. The stochastic noise approach allows us to account for the effect such noise has on the estimation of parameters (such as rate) and their uncertainties. Furthermore, stochastic models can provide help in the search for physical explanations of the observed signal. We examine some models of expected signals to examine their suitability as candidates for the noise in a stochastic sense. At the present it appears that certain models characterise the height time series at specific frequencies, that is, annual and its harmonics.
DE: 1206 Crustal movements--interplate (8155)
DE: 1208 Crustal movements--intraplate (8110)
DE: 1244 Standards and absolute measurements
DE: 1294 Instruments and techniques
DE: 1299 General or miscellaneous
SC: Geodesy [G]
MN: 2003 Fall Meeting