Effect of the span of Australian GPS coordinate time series in establishing an optimal noise model

Jiang, Weiping; Zhou, Xiaohui

doi:10.1007/s11430-014-4996-z

Cited by 10 publications

(5 citation statements)

References 29 publications

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“…6000 km. Similar studies were performed for stations distant at the 10 3 km level, located in China (Li et al 2015;Shen et al 2013) or in Australia (Jiang and Zhou 2015).…”

Section: Gnss Time Seriesmentioning

confidence: 55%

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Gruszczynski¹,

Kłos²,

Bogusz³

2018

Pageoph Topical Volumes

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For the first time, we introduced the probabilistic principal component analysis (pPCA) regarding the spatio-temporal filtering of Global Navigation Satellite System (GNSS) position time series to estimate and remove Common Mode Error (CME) without the interpolation of missing values. We used data from the International GNSS Service (IGS) stations which contributed to the latest International Terrestrial Reference Frame (ITRF2014). The efficiency of the proposed algorithm was tested on the simulated incomplete time series, then CME was estimated for a set of 25 stations located in Central Europe. The newly applied pPCA was compared with previously used algorithms, which showed that this method is capable of resolving the problem of proper spatio-temporal filtering of GNSS time series characterized by different observation time span. We showed, that filtering can be carried out with pPCA method when there exist two time series in the dataset having less than 100 common epoch of observations. The 1st Principal Component (PC) explained more than 36% of the total variance represented by time series residuals' (series with deterministic model removed), what compared to the other PCs variances (less than 8%) means that common signals are significant in GNSS residuals. A clear improvement in the spectral indices of the power-law noise was noticed for the Up component, which is reflected by an average shift towards white noise from-0.98 to-0.67 (30%). We observed a significant average reduction in the accuracy of stations' velocity estimated for filtered residuals by 35, 28 and 69% for the North, East, and Up components, respectively. CME series were also subjected to analysis in the context of environmental mass loading influences of the filtering results. Subtraction of the environmental loading models from GNSS residuals provides to reduction of the estimated CME variance by 20 and 65% for horizontal and vertical components, respectively.

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“…6000 km. Similar studies were performed for stations distant at the 10 3 km level, located in China (Li et al 2015;Shen et al 2013) or in Australia (Jiang and Zhou 2015).…”

Section: Gnss Time Seriesmentioning

confidence: 55%

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

Gruszczynski¹,

Kłos²,

Bogusz³

2018

Pageoph Topical Volumes

View full text Add to dashboard Cite

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“…During data preprocessing, obvious “jumps” caused by the replacement of antennas are estimated and removed. At the same time, residuals of greater than three times of standard deviations after removing linear trend of the coordinate time series are considered as gross errors and eliminated (Jiang & Zhou, 2015). Due to limited space, here we only list the coordinate time series of six representative stations in Figure 2, e.g., station BJFS, XJBE, HBXF, YNJP, HRBN, and XIAM in the North, Northwest, Central, Southwest, Northeast, and Southeast region of China, respectively.…”

Section: Methodsmentioning

confidence: 99%

A New Three‐Dimensional Noise Modeling Method Based on Singular Value Decomposition and Its Application to CMONOC GPS Network

Jiang

et al. 2021

Earth and Space Science

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Construction of noise model is an important task in the analysis of Global Positioning System (GPS) reference station coordinate time series. Ignoring the relationship between the noise on different components within a GPS station network may affect the accuracy of station's velocity and its uncertainty. In view of this problem, we propose to use the singular value decomposition (SVD) method to establish a new three‐dimensional (3‐D) noise model for GPS station networks. Our simulation tests show that the accuracy of the noise amplitude obtained based on the proposed 3‐D noise model is 30–50% higher than that directly obtained from Create and Analyze Time Series (CATS) software, thereby improving the accuracy of the velocity uncertainty by approximately two times. Taking the coordinate time series of 82 GPS stations from the Crustal Motion Observation Network of China (CMONOC) as an example, we confirm that significant correlation exists among noise amplitude estimates in the different components of the CMONOC stations. In general, the variation of white noise (WN) amplitude is 2–6% smaller than that of flicker noise (FN) amplitude, and the FN amplitude in the vertical component is 1% larger than that in the horizontal component. Compared with the velocity uncertainty obtained from CATS software, the variations of the velocity uncertainty obtained from the new 3‐D noise model in the horizontal components (7% for North, 8% for East) are slightly less than that in the Up component (9%). However, the velocity estimation is hardly affected by the new 3‐D noise model.

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“…To quantitatively analyze the coordinate time series variations, we simultaneously compute the velocity, the amplitudes and phases of the seasonal signals. Then, a function model that is widely used in analysis of GNSS coordinate time series is adopted [22,[36][37][38]. The model contains a linear velocity, an offset, harmonics of the tropical year oscillation and noise.…”

Section: The Processing Of the Vertical Deformation Time Seriesmentioning

confidence: 99%

Vertical Deformation Monitoring of the Suspension Bridge Tower Using GNSS: A Case Study of the Forth Road Bridge in the UK

et al. 2018

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The vertical deformation monitoring of a suspension bridge tower is of paramount importance to maintain the operational safety since nearly all forces are eventually transferred as the vertical stress on the tower. This paper analyses the components affecting the vertical deformation and attempts to reveal its deformation mechanism. Firstly, we designed a strategy for high-precision GNSS data processing aiming at facilitating deformation extraction and analysis. Then, 33 months of vertical deformation time series of the southern tower of the Forth Road Bridge (FRB) in the UK were processed, and the accurate subsidence and the parameters of seasonal signals were estimated based on a classic function model that has been widely studied to analyse GNSS coordinate time series. We found that the subsidence rate is about 4.7 mm/year, with 0.1 mm uncertainty. Meanwhile, a 15-month meteorological dataset was utilised with a thermal expansion model (TEM) to explain the effects of seasonal signals on tower deformation. The amplitude of the annual signals correlated quite well that obtained by the TEM, with the consistency reaching 98.9%, demonstrating that the thermal effect contributes significantly to the annual signals. The amplitude of daily signals displays poor consistency with the ambient temperature data. However, the phase variation tendencies between the daily signals of the vertical deformation and the ambient temperature are highly consistent after February 2016. Finally, the potential contribution of the North Atlantic Drift (NAD) to the characteristics of annual and daily signals is discussed because of the special geographical location of the FRB. Meanwhile, this paper emphasizes the importance of collecting more detailed meteorological and other loading data for the investigation of the vertical deformation mechanism of the bridge towers over time with the support of GNSS.

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Effect of the span of Australian GPS coordinate time series in establishing an optimal noise model

Cited by 10 publications

References 29 publications

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

A Filtering of Incomplete GNSS Position Time Series with Probabilistic Principal Component Analysis

A New Three‐Dimensional Noise Modeling Method Based on Singular Value Decomposition and Its Application to CMONOC GPS Network

Vertical Deformation Monitoring of the Suspension Bridge Tower Using GNSS: A Case Study of the Forth Road Bridge in the UK

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