Characteristics of Daily Position Time Series from the Hong Kong Gps Fiducial Network

Yuan, Linguo; Ding, Xiaoli; Chen, Wu; Kwok, Simon; Chan, Siu Bun; Hung, Pen Shan; Chau, Kam Tim

doi:10.1002/cjg2.1292

Cited by 47 publications

(40 citation statements)

References 22 publications

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“…The processing includes the following corrections: ATML, SMML, NTOL and SCML based on QLM dataset (Yuan et al, 2008;Jiang et al, 2013;He et al, 2015). The QLM dataset described as follows (for me detail see Dong et al, 2002;Yuan et al, 2008;Jiang et al, 2013;He et al, 2015).…”

Section: Environmental Loading Dataset and Processingmentioning

confidence: 99%

Noise analysis for environmental loading effect on GPS time series

He¹

2016

Acta Geodynamica Et Geomaterialia

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This paper focusses on the impact of environment mass loading on GPS time-series of position changes and noise characteristic. We make use of position time series of 206 GPS station ranging from 2001 to 2013 globally distributed. Firstly, we investigate the spatio-temporal pattern of mass loadings based on QLM dataset. The results show that loading effect is significant for short time series, and the instantaneous impact cannot be ignored especially in high-precision geophysical studies. Meanwhile, the results indicate that loading effect behaves with regional difference in spatial scale, and this explains why there exist significant differences on the contribution of environmental loading on GPS time series in the scientific community. Secondly, among 90 % of the stations the weighted root mean square (WRMS) is reduced after applying the loading correction based on QLM model, improving the accuracy of GPS time series. Finally, we quantify the stochastic of GPS time series. The results show that the noise model of GPS time series can be described by a various combination of those models, mainly by FN+WN model and PL+WN model. From our statistics, we can conclude that the best noise models are either white and flicker or white and power-law. Environment loading has a significant impact on the stochastic noise properties of GPS time series, on 33 %, 15 %, and 39 % of the station's noise properties (noise types) have been changed after loading correction for NEU components, respectively. Environment loading is one of the factor that 'raw' GPS time series exhibited Gauss Markov noise properties. Furthermore, the impact of environmental loading on GPS site velocity cannot be ignored; this is particularly significant for the vertical component. ARTICLE INFO

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Section: Environmental Loading Dataset and Processingmentioning

confidence: 99%

Noise analysis for environmental loading effect on GPS time series

He¹

2016

Acta Geodynamica Et Geomaterialia

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“…Many regional station networks have been established for monitoring the crust deformation and water vapor contents using global navigation satellite system (GNSS) Yuan et al 2008;Nilsson and Elgered 2008;Lee et al 2013;Ji et al 2014). The dominant noises in GNSS position time series are white, flicker and random walk noises Langbein and Johnson 1997;Mao et al 1999;Williams et al 2004;Langbein 2004).…”

Section: Introductionmentioning

confidence: 99%

Weighted spatiotemporal filtering using principal component analysis for analyzing regional GNSS position time series

Shen

2015

Acta Geod Geophys

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Principal component analysis (PCA) is a powerful tool for extracting common mode errors from the position time series of a regional station network determined by global navigation satellite system (GNSS). It is implicitly based on the assumption that a time series dataset contains temporally uniform white noise. Since the position time series of a regional station network are not uniform and could have data gaps, this paper develops a PCA-based weighted spatiotemporal filtering (WSF) approach by taking into account the positioning formal error of daily solution and the data gaps in time series. The position time series of 27 GNSS stations of the Crust Motion Observation Network of China are analyzed to demonstrate the performance of WSF approach, and also compared with the modified PCA technique in Shen et al. (J Geod 88:1-12, 2014). It shows that the WSF approach outperforms the modified PCA at 21, 19 and 17 out of the total 27 stations for north, east and up components, respectively. The average formal standard deviation of unit weight derived from WSF and modified PCA are 2.12, 2.42, 5.88 and 2.21, 2.52, 6.05 for north, east and up components, respectively; the relative improvements are 4.1, 4.0 and 2.8 %. Moreover, two simulations of a network with 4 stations are processed to show the performance of WSF. The results show that WSF provides better results for all coordinate components of all stations when the local effects are small or negligible. For cases when the local effects becoming larger, the WSF performs better than the modified PCA from the statistical point of view. From the real and synthetic time series analysis results, it is reasonable to conclude that the positioning formal error of daily solution should be considered in spatiotemporal filtering.

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“…In the spatiotemporal filtering using PCA, CME can be got with varying spatial responses, providing a more solid numerical framework for analyzing the physical source in the time series. Yuan et al (2008) found that the cycle items of CME got by PCA filtering in vertical direction decreased significantly after a load mass correction, suggesting that non-tectonic deformation is the main cause of CME in vertical direction. In addition, as an effective spatiotemporal analysis method, PCA has also been used in the field of earth geophysics for various purposes (Kawamura and Yamaoka 2006;Tiampo et al 2004).…”

Section: Introductionmentioning

confidence: 94%

Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis

et al. 2015

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GPS has been widely used in the field of geodesy and geodynamics thanks to its technology development and the improvement of positioning accuracy. A time series observed by GPS in vertical direction usually contains tectonic signals, non-tectonic signals, residual atmospheric delay, measurement noise, etc. Analyzing these information is the basis of crustal deformation research. Furthermore, analyzing the GPS time series and extracting the non-tectonic information are helpful to study the effect of various geophysical events. Principal component analysis (PCA) is an effective tool for spatiotemporal filtering and GPS time series analysis. But as it is unable to extract statistically independent components, PCA is unfavorable for achieving the implicit information in time series. Independent component analysis (ICA) is a statistical method of blind source separation (BSS) and can separate original signals from mixed observations. In this paper, ICA is used as a spatiotemporal filtering method to analyze the spatial and temporal features of vertical GPS coordinate time series in the UK and Sichuan-Yunnan region in China. Meanwhile, the contributions from atmospheric and soil moisture mass loading are evaluated. The analysis of the relevance between the independent components and mass loading with their spatial distribution shows that the signals extracted by ICA have a strong correlation with the non-tectonic deformation, indicating that ICA has a better performance in spatiotemporal analysis.

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Characteristics of Daily Position Time Series from the Hong Kong Gps Fiducial Network

Cited by 47 publications

References 22 publications

Noise analysis for environmental loading effect on GPS time series

Noise analysis for environmental loading effect on GPS time series

Weighted spatiotemporal filtering using principal component analysis for analyzing regional GNSS position time series

Spatiotemporal analysis of GPS time series in vertical direction using independent component analysis

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