scholarly journals Outlier detection in GNSS position time series

2016 ◽  
Vol 19 (2) ◽  
pp. 43-50
Author(s):  
Trong Dinh Tran ◽  
Toan Duy Dao ◽  
Tung So Vu ◽  
Dung Ngoc Luong ◽  
Chieu Dinh Vu ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Variation ◽  
Outlier Detection ◽  
Visual Interpretation ◽  
Position Time Series ◽  
Recorded Data ◽  
Gnss Position Time Series ◽  
Tectonic Activities

The continuous GNSS stations are used to determine the displacement velocities, seasonal variation, amplitude of tectonic activities,… To accurately determine these factors, the first is to remove outliers in GNSS position time series. In general, filtering approaches are subjectively selected based on the experience and visual interpretation of experts. Therefore, the process may lead to a waste of time or confusion, especially for stations with long-term continuously recorded data. The purpose of paper is to introduce the applicability of several algorithms and methods of filtering outliers in GNSS position time series.

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>

scholarly journals Automatic Generation of Sentinel-1 Continental Scale DInSAR Deformation Time Series through an Extended P-SBAS Processing Pipeline in a Cloud Computing Environment

2020 ◽  
Vol 12 (18) ◽  
pp. 2961 ◽  
Author(s):  
Riccardo Lanari ◽  
Manuela Bonano ◽  
Francesco Casu ◽  
Claudio De Luca ◽  
Michele Manunta ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Computing ◽  
Ground Deformation ◽  
Information Access ◽  
Automatic Generation ◽  
Continental Scale ◽  
Processing Pipeline ◽  
Position Time Series ◽  
Sar Data ◽  
Gnss Position Time Series

We present in this work an advanced processing pipeline for continental scale differential synthetic aperture radar (DInSAR) deformation time series generation, which is based on the parallel small baseline subset (P-SBAS) approach and on the joint exploitation of Sentinel-1 (S-1) interferometric wide swath (IWS) SAR data, continuous global navigation satellite system (GNSS) position time-series, and cloud computing (CC) resources. We first briefly describe the basic rationale of the adopted P-SBAS processing approach, tailored to deal with S-1 IWS SAR data and to be implemented in a CC environment, highlighting the innovative solutions that have been introduced in the processing chain we present. They mainly consist in a series of procedures that properly exploit the available GNSS time series with the aim of identifying and filtering out possible residual atmospheric artifacts that may affect the DInSAR measurements. Moreover, significant efforts have been carried out to improve the P-SBAS processing pipeline automation and robustness, which represent crucial issues for interferometric continental scale analysis. Then, a massive experimental analysis is presented. In this case, we exploit: (i) the whole archive of S-1 IWS SAR images acquired over a large portion of Europe, from descending orbits, (ii) the continuous GNSS position time series provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL) available for the investigated area, and (iii) the ONDA platform, one of the Copernicus Data and Information Access Services (DIAS). The achieved results demonstrate the capability of the proposed solution to successfully retrieve the DInSAR time series relevant to such a huge area, opening new scenarios for the analysis and interpretation of these ground deformation measurements.

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