Mon Oct 13, F. Flechtner
• Sorted by Date • Sorted by Last Name of First Author •
Yan, Jun, Dong, Danan, Bürgmann, Roland, Materna, Kathryn, Tan, Weijie, Peng, Yu, and Chen, Junping, 2019. Separation of Sources of Seasonal Uplift in China Using Independent Component Analysis of GNSS Time Series. Journal of Geophysical Research (Solid Earth), 124(11):11,951–11,971, doi:10.1029/2019JB018139.
• from the NASA Astrophysics Data System • by the DOI System •
@ARTICLE{2019JGRB..12411951Y,
author = {{Yan}, Jun and {Dong}, Danan and {B{\"u}rgmann}, Roland and {Materna}, Kathryn and {Tan}, Weijie and {Peng}, Yu and {Chen}, Junping},
title = "{Separation of Sources of Seasonal Uplift in China Using Independent Component Analysis of GNSS Time Series}",
journal = {Journal of Geophysical Research (Solid Earth)},
keywords = {GNSS time series, China, Seasonal mass loading, Independent component analysis, Principal component analysis, QOCA},
year = 2019,
month = nov,
volume = {124},
number = {11},
pages = {11,951-11,971},
abstract = "{With the improvement of Global Navigation Satellite System (GNSS)
observation accuracy and the establishment of large continuously
operating networks, long GNSS time series are now widely used to
understand a range of Earth deformation processes. The
continuously operating stations of the Crustal Movement
Observation Network of China capture deformation signals due to
time-dependent tectonic, nontectonic mass loading, and
potentially unknown geophysical processes. In order to separate
and recover these underlying sources accurately and effectively,
we apply the independent component analysis (ICA) to decompose
the observed time series of vertical displacements. Then, we
compare these signals with those predicted from independently
developed geophysical process models of atmospheric, nontidal
ocean, snow, soil moisture mass loading, and the Land Surface
Discharge Model, as well as with Gravity Recovery and Climate
Experiment observations. For comparison, we also perform the
principal component analysis decomposition of time series and
find that the ICA achieves a more consistent representation of
multiple geophysical contributors to annual vertical GNSS
displacements. ICA can decompose the long-term trend and
different seasonal and multiannual signals that closely
correspond to the independently derived mass loading models. We
find that independent contributions from atmospheric, soil
moisture, and snow mass loading can be resolved from the GNSS
data. Discrepancies are likely due to the correlated nature of
some of the loading processes and unmodeled contributions from
groundwater and surface water changes in South Central China and
the Ganges Basin.}",
doi = {10.1029/2019JB018139},
adsurl = {https://ui.adsabs.harvard.edu/abs/2019JGRB..12411951Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
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