scholarly journals Influence of non-tidal atmospheric and oceanic loading deformation on the stochastic properties of over 10,000 GNSS vertical land motion time series

2021 ◽  
Author(s):  
Kevin Gobron ◽  
Paul Rebischung ◽  
Olivier de Viron ◽  
Michel Van Camp ◽  
Alain Demoulin
Keyword(s):  
Time Series ◽  
White Noise ◽  
Power Law ◽  
Noise Amplitude ◽  
Vertical Land Motion ◽  
Motion Time ◽  
Stochastic Parameters ◽  
Position Time Series ◽  
Stochastic Properties ◽  
Gnss Position Time Series

<p>Over the past two decades, numerous studies demonstrated that the stochastic variability in GNSS position time series – often referred to as noise – is both temporally and spatially correlated. The time correlation of this stochastic variability can be well approximated by a linear combination of white noise and power-law stochastic processes with different amplitudes. Although acknowledged in many geodetic studies, the presence of such power-law processes in GNSS position time series remains largely unexplained. Considering that these power-law processes are the primary source of uncertainty for velocity estimates, it is crucial to identify their origin(s) and to try to reduce their influence on position time series.</p><p> </p><p>Using the Least-Squares Variance Component Estimation method, we analysed the influence of removing surface mass loading deformation on the stochastic properties of vertical land motion time series (VLMs). We used the position time series of over 10,000 globally distributed GNSS stations processed by the Nevada Geodetic Laboratory at the University of Nevada, Reno, and loading deformation time series computed by the Earth System Modelling (ESM) team at GFZ-Potsdam. Our results show that the values of stochastic parameters, namely, white noise amplitude, spectral index, and power-law noise amplitude, but also the spatial correlation, are systematically influenced by non-tidal atmospheric and oceanic loading deformation. The observed change in stochastic parameters often translates into a reduction of trend uncertainties, reaching up to -75% when non-tidal atmospheric and oceanic loading deformation is highest.</p>

scholarly journals STOCHASTIC MODEL OF THE BRAZILIAN GPS NETWORK COORDINATES TIME SERIES

2018 ◽  
Vol 24 (4) ◽  
pp. 545-563
Author(s):  
Christian Gonzalo Pilapanta Amagua ◽  
Claudia Pereira Krueger ◽  
Alfonso Rodrigo Tierra Criollo
Keyword(s):  
Time Series ◽  
Random Walk ◽  
White Noise ◽  
Power Law ◽  
Fractional Power ◽  
Noise Model ◽  
Coastal Zones ◽  
White Noise Process ◽  
Noise Process ◽  
Stochastic Properties

Abstract It is well known that daily estimates of GPS coordinates are highly temporally correlated and that the knowledge and understanding of this correlation allows to establish more realistic uncertainties of the parameters estimated from the data. Despite this, there are currently no studies related to the analysis and calculation of the noise sources in geodetic time series in Brazil. In this context, this paper focuses on the investigation of the stochastic properties of a total of 486 coordinates time series from 159 GPS stations belonging to the Brazilian Network for Continuous Monitoring of GNSS (RBMC) using the maximum likelihood estimation approach. To reliably describe the GPS time series, we evaluate 4 possible stochastic models as models of each time series: 3 models with integer spectral indices (white noise, flicker plus white noise and random-walk plus white noise model) and 1 with fractional spectral index (fractional power-law plus white noise). By comparing the calculated noise content values for each model, it is possible to demonstrate a stepwise increase of the noise content, being the combination of a fractional power-law process and white noise process, the model with smaller values and the combination of random walk process with white noise process, the model with greater values. The analysis of the spatial distribution of the noise values of the processes allow demonstrate that the GPS sites with the highest accumulated noise values, coincide with sites located in coastal zones and river basins and that their stochastic properties can be aliased by the occurrence of different physical signals typical of this type of zones, as the case of the hydrological loading effect.

scholarly journals Analysis of Noise and Velocity in GNSS EPN-Repro 2 Time Series

2021 ◽  
Vol 13 (14) ◽  
pp. 2783
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Kamil Maciuk ◽  
Jacek Kudrys ◽  
Eduard Ilie Nastase ◽  
...  
Keyword(s):  
Time Series ◽  
Visual Inspection ◽  
Flicker Noise ◽  
Vertical Component ◽  
Likelihood Estimation ◽  
Noise Model ◽  
Noise Amplitude ◽  
Allan Deviation ◽  
Power Spectral ◽  
Position Time Series

This study evaluates the EUREF Permanent Network (EPN) station position time series of approximately 200 GNSS stations subject to the Repro 2 reprocessing campaign in order to characterize the dominant types of noise and amplitude and their impact on estimated velocity values and associated uncertainties. The visual inspection on how different noise model represents the analysed data was done using the power spectral density of the residuals and the estimated noise model and it is coherent with the calculated Allan deviation (ADEV)-white and flicker noise. The velocities resulted from the dominant noise model are compared to the velocity obtained by using the Median Interannual Difference Adjusted for Skewness (MIDAS). The results show that only 3 stations present a dominant random walk noise model compared to flicker and powerlaw noise model for the horizontal and vertical components. We concluded that the velocities for the horizontal and vertical component show similar values in the case of MIDAS and maximum likelihood estimation (MLE), but we also found that the associated uncertainties from MIDAS are higher compared to the uncertainties from MLE. Additionally, we concluded that there is a spatial correlation in noise amplitude, and also regarding the differences in velocity uncertainties for the Up component.

scholarly journals Extracting Common Mode Errors of Regional GNSS Position Time Series in the Presence of Missing Data by Variational Bayesian Principal Component Analysis

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2298 ◽  
Author(s):  
Wudong Li ◽  
Weiping Jiang ◽  
Zhao Li ◽  
Hua Chen ◽  
Qusen Chen ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Missing Data ◽  
Principal Component ◽  
Component Analysis ◽  
Common Mode ◽  
Variational Bayesian ◽  
North East ◽  
Position Time Series ◽  
Gnss Position Time Series

Removal of the common mode error (CME) is very important for the investigation of global navigation satellite systems’ (GNSS) error and the estimation of an accurate GNSS velocity field for geodynamic applications. The commonly used spatiotemporal filtering methods normally process the evenly spaced time series without missing data. In this article, we present the variational Bayesian principal component analysis (VBPCA) to estimate and extract CME from the incomplete GNSS position time series. The VBPCA method can naturally handle missing data in the Bayesian framework and utilizes the variational expectation-maximization iterative algorithm to search each principal subspace. Moreover, it could automatically select the optimal number of principal components for data reconstruction and avoid the overfitting problem. To evaluate the performance of the VBPCA algorithm for extracting CME, 44 continuous GNSS stations located in Southern California were selected. Compared to previous approaches, VBPCA could achieve better performance with lower CME relative errors when more missing data exists. Since the first principal component (PC) extracted by VBPCA is remarkably larger than the other components, and its corresponding spatial response presents nearly uniform distribution, we only use the first PC and its eigenvector to reconstruct the CME for each station. After filtering out CME, the interstation correlation coefficients are significantly reduced from 0.43, 0.46, and 0.38 to 0.11, 0.10, and 0.08, for the north, east, and up (NEU) components, respectively. The root mean square (RMS) values of the residual time series and the colored noise amplitudes for the NEU components are also greatly suppressed, with average reductions of 27.11%, 28.15%, and 23.28% for the former, and 49.90%, 54.56%, and 49.75% for the latter. Moreover, the velocity estimates are more reliable and precise after removing CME, with average uncertainty reductions of 51.95%, 57.31%, and 49.92% for the NEU components, respectively. All these results indicate that the VBPCA method is an alternative and efficient way to extract CME from regional GNSS position time series in the presence of missing data. Further work is still required to consider the effect of formal errors on the CME extraction during the VBPCA implementation.

A data-driven approach for denoising GNSS position time series

2017 ◽  
Vol 92 (8) ◽  
pp. 905-922 ◽  
Author(s):  
Yanyan Li ◽  
Caijun Xu ◽  
Lei Yi ◽  
Rongxin Fang
Keyword(s):  
Time Series ◽  
Data Driven ◽  
Data Driven Approach ◽  
Position Time Series ◽  
Gnss Position Time Series

Automatic offset detection using R open source libraries

2021 ◽  
Author(s):  
Shambo Bhattacharjee ◽  
Alvaro Santamaría-Gómez
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Open Source ◽  
Change Point ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data ◽  
Position Time Series ◽  
Gnss Time Series ◽  
Gnss Position Time Series

<p>Long GNSS position time series contain offsets typically at rates between 1 and 3 offsets per decade. We may classify the offsets whether their epoch is precisely known, from GNSS station log files or Earthquake databases, or unknown. Very often, GNSS position time series contain offsets for which the epoch is not known a priori and, therefore, an offset detection/removal operation needs to be done in order to produce continuous position time series needed for many applications in geodesy and geophysics. A further classification of the offsets corresponds to those having a physical origin related to the instantaneous displacement of the GNSS antenna phase center (from Earthquakes, antenna changes or even changes of the environment of the antenna) and those spurious originated from the offset detection method being used (manual/supervised or automatic/unsupervised). Offsets due to changes of the antenna and its environment must be avoided by the station operators as much as possible. Spurious offsets due to the detection method must be avoided by the time series analyst and are the focus of this work.</p><p><br>Even if manual offset detection by expert analysis is likely to perform better, automatic offset detection algorithms are extremely useful when using massive (thousands) GNSS time series sets. Change point detection and cluster analysis algorithms can be used for detecting offsets in a GNSS time series data and R offers a number of libraries related to performing these two. For example, the “Bayesian Analysis of Change Point Problems” or the “bcp” helps to detect change points in a time series data. Similarly, the “dtwclust” (Dynamic Time Warping algorithm) is used for the time series cluster analysis. Our objective is to assess various open-source R libraries for the automatic offset detection.</p>

Sign in / Sign up

Export Citation Format

Share Document