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Zannat, Umma Jamila and Tregoning, Paul, 2017. Estimating network effect in geocenter motion: Applications. Journal of Geophysical Research (Solid Earth), 122(10):8347–8359, doi:10.1002/2017JB014247.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2017JGRB..122.8347Z,
author = {{Zannat}, Umma Jamila and {Tregoning}, Paul},
title = "{Estimating network effect in geocenter motion: Applications}",
journal = {Journal of Geophysical Research (Solid Earth)},
keywords = {network effect, geocenter motion, geodesy, reference frame, coseismic deformation, hydrological loading},
year = 2017,
month = oct,
volume = {122},
number = {10},
pages = {8347-8359},
abstract = "{The network effect is the error associated with the subsampling of the
Earth surface by space geodetic networks. It is an obstacle
toward the precise measurement of geocenter motion, that is, the
relative motion between the center of mass of the Earth system
and the center of figure of the Earth surface. In a
complementary paper, we proposed a theoretical approach to
estimate the magnitude of this effect from the displacement
fields predicted by geophysical models. Here we evaluate the
effectiveness of our estimate for two illustrative physical
processes: coseismic displacements inducing instantaneous
changes in the Helmert parameters and elastic deformation due to
surface water movements causing secular drifts in those
parameters. For the first, we consider simplified models of the
2004 Sumatra-Andaman and the 2011 T{\={o}}hoku-Oki earthquakes,
and for the second, we use the observations of the Gravity
Recovery and Climate Experiment, complemented by an ocean model.
In both case studies, it is found that the magnitude of the
network effect, even for a large global network, is often as
large as the magnitude of the changes in the Helmert parameters
themselves. However, we also show that our proposed modification
to the definition of the center of network frame to include
weights proportional to the area of the Earth surface that the
stations represent can significantly reduce the network effect
in most cases.}",
doi = {10.1002/2017JB014247},
adsurl = {https://ui.adsabs.harvard.edu/abs/2017JGRB..122.8347Z},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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