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Acta Geodynamica et Geomaterialia

Authors: He Xiaoxing, Montillet Jean-Philippe, Hua Xianghong, Yu Kegen, Jiang Weiping and Zhou Feng
DOI: 10.13168/AGG.2016.0034
Journal: Acta Geodynamica et Geomaterialia, Vol. 14, No. 1 (185), Prague 2017
Full Text: PDF file (2.3 MB)
Keywords: environmental loading effect; noise analysis; Maximum Likelihood Estimation
Abstract: 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.