scholarly journals Potential Contributors to Common Mode Error in Array GPS Displacement Fields in Taiwan Island

2021 ◽  
Vol 13 (21) ◽  
pp. 4221
Author(s):  
Xiaojun Ma ◽  
Bin Liu ◽  
Wujiao Dai ◽  
Cuilin Kuang ◽  
Xuemin Xing
Keyword(s):  
Time Series ◽  
Component Analysis ◽  
Mass Loading ◽  
Common Mode ◽  
Temporal Filtering ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Common Mode Error ◽  
Taiwan Island ◽  
Spatio Temporal

The existence of the common mode error (CME) in the continuous global navigation satellite system (GNSS) coordinate time series affects geophysical studies that use GNSS observations. To understand the potential contributors of CME in GNSS networks in Taiwan and their effect on velocity estimations, we used the principal component analysis (PCA) and independent component analysis (ICA) to filter the vertical coordinate time series from 44 high-quality GNSS stations in Taiwan island in China, with a span of 10 years. The filtering effects have been evaluated and the potential causes of the CME are analyzed. The root-mean-square values decreased by approximately 14% and 17% after spatio-temporal filtering using PCA and ICA, respectively. We then discuss the relationship between the CME sources obtained by ICA and the environmental loads. The results reveal that the independent displacements extracted by ICA correlate with the atmospheric mass loading (ATML) and land water storage mass loading (LWS) of Taiwan in terms of both its amplitude and phase. We then use the white noise plus power law noise model to quantitatively estimate the noise characteristics of the pre- and post-filtered coordinate time series based on the maximum likelihood estimation criterion. The results indicate that spatio-temporal filtering reduces the amplitude of the PL and the periodic terms in the GPS time series.

scholarly journals Analysis of the Potential Contributors to Common Mode Error in Chuandian Region of China

2020 ◽  
Vol 12 (5) ◽  
pp. 751
Author(s):  
Weijie Tan ◽  
Junping Chen ◽  
Danan Dong ◽  
Weijing Qu ◽  
Xueqing Xu
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Principal Component ◽  
Mass Loading ◽  
Common Mode ◽  
Surface Mass ◽  
Common Mode Error ◽  
Comparison Results ◽  
Wide Range ◽  
Gps Time Series

Common mode error (CME) in Chuandian region of China is derived from 6-year continuous GPS time series and is identified by principal component analysis (PCA) method. It is revealed that the temporal behavior of the CME is not purely random, and contains unmodeled signals such as nonseasonal mass loadings. Its spatial distribution is quite uniform for all GPS sites in the region, and the first principal component, uniformly distributed in the region, has a spatial response of more than 70%. To further explore the potential contributors of CME, daily atmospheric mass loading and soil moisture mass loading effects are evaluated. Our results show that ~15% of CME can be explained by these daily surface mass loadings. The power spectral analysis is used to assess the CME. After removing atmospheric and soil moisture loadings from the CME, the power of the CME reduces in a wide range of frequencies. We also investigate the contribution of CME in GPS filtered residuals time series and it shows the Root Mean Squares (RMSs) of GPS time series are reduced by applying of the mass loading corrections in CME. These comparison results demonstrate that daily atmosphere pressure and the soil moisture mass loadings are a part of contributors to the CME in Chuandian region of China.

Quantitative analysis of geophysical sources of common mode component in CMONOC GPS coordinate time series

2017 ◽  
Vol 60 (12) ◽  
pp. 2896-2909 ◽  
Author(s):  
Zhaohan Zhu ◽  
Xiaohui Zhou ◽  
Liansheng Deng ◽  
Kaihua Wang ◽  
Boye Zhou
Keyword(s):  
Time Series ◽  
Quantitative Analysis ◽  
Common Mode ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Gps Coordinate Time Series

scholarly journals Modeling Seasonal Variations in Vertical GPS Coordinate Time Series Using Independent Component Analysis and Varying Coefficient Regression

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5627
Author(s):  
Bin Liu ◽  
Xuemin Xing ◽  
Jianbo Tan ◽  
Qing Xia
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Seasonal Variations ◽  
Component Analysis ◽  
Independent Component ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
Varying Coefficient ◽  
The Common ◽  
Gps Coordinate Time Series

Common seasonal variations in Global Positioning System (GPS) coordinate time series always exist, and the modeling and correction of the seasonal signals are helpful for many geodetic studies using GPS observations. A spatiotemporal model was proposed to model the common seasonal variations in vertical GPS coordinate time series, based on independent component analysis and varying coefficient regression method. In the model, independent component analysis (ICA) is used to separate the common seasonal signals in the vertical GPS coordinate time series. Considering that the periodic signals in GPS coordinate time series change with time, a varying coefficient regression method is used to fit the separated independent components. The spatiotemporal model was then used to fit the vertical GPS coordinate time series of 262 global International GPS Service for Geodynamics (IGS) GPS sites. The results show that compared with least squares regression, the varying coefficient method can achieve a more reliable fitting result for the seasonal variation of the separated independent components. The proposed method can accurately model the common seasonal variations in the vertical GPS coordinate time series, with an average root mean square (RMS) reduction of 41.6% after the model correction.

scholarly journals Impacts of Local Effects and Surface Loads on the Common Mode Error Filtering in Continuous GPS Measurements in the Northwest of Yunnan Province, China

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5408
Author(s):  
Keliang Zhang ◽  
Yuebing Wang ◽  
Weijun Gan ◽  
Shiming Liang
Keyword(s):  
Time Series ◽  
Yunnan Province ◽  
Vertical Component ◽  
Mass Loading ◽  
Gps Measurements ◽  
Local Effects ◽  
Common Mode ◽  
Common Mode Error ◽  
Grace Data ◽  
The Common

While seasonal hydrological mass loading, derived from Gravity Recovery and Climate Experiment (GRACE) measurements, shows coherent spatial patterns and is an important source for the common mode error (CME) in continuous global positioning system (cGPS) measurements in Yunnan, it is a challenge to quantify local effects and detailed changes in daily GPS measurements by using GRACE data due to its low time and spatial resolutions. In this study, we computed and compared two groups of CMEs for nine cGPS sites in the northwest Yunnan province; rCMEs were computed with the residual cGPS time series having high inter-station correlations, while oCMEs were computed with all the GPS time series. The rCMEs-filtered time series had smaller variances and larger root mean square (RMS) reductions than those that were oCMEs-filtered, and when the stations local effects were not removed, spurious transient-like signals occurred. Compared with hydrological mass loading (HYDL), its combination with non-tidal atmosphere pressure and ocean mass reached a better agreement with the CME in the vertical component, with the Nash–Sutcliffe efficiency (NSE) increasing from 0.28 to 0.55 and the RMS reduction increasing from 15.19% to 33.4%, respectively. Our results suggest that it is necessary to evaluate the inter-station correlation and remove the possible noisy stations before conducting CME filtering, and that one should carefully choose surface loading models to correct the raw cGPS time series if CME filtering is not conducted.

scholarly journals A Sub-Regional Extraction Method of Common Mode Components from IGS and CMONOC Stations in China

2019 ◽  
Vol 11 (11) ◽  
pp. 1389 ◽  
Author(s):  
Shuguang Wu ◽  
Guigen Nie ◽  
Jingnan Liu ◽  
Kezhi Wang ◽  
Changhu Xue ◽  
...  
Keyword(s):  
Time Series ◽  
Signal To Noise Ratio ◽  
Principal Component ◽  
Satellite System ◽  
Common Mode ◽  
Coordinate Time ◽  
Coordinate Time Series ◽  
North East ◽  
Comparison Results ◽  
Gps Coordinate Time Series

There is always a need to extract more accurate regional common mode component (CMC) series from coordinate time series of Global Positioning System (GPS) stations, which would be of great benefit to describe the deformation features of the Earth’s surface with more reliability. For this purpose, this paper combines all 11 International Global Navigation Satellite System (GNSS) Service (IGS) stations in China with over 70 stations selected from the Crustal Movement Observation Network of China (CMONOC) to compute CMC series of IGS stations by using a principal component analysis (PCA) method under cases of one whole region and eight sub-regions. The comparison results show that the percentage of first-order principal component (PC1) in North, East and Up components increase by 10.8%, 16.1% and 25.1%, respectively, after dividing the whole China region into eight sub-regions. Meanwhile, Root Mean Square (RMS) reduction rates of residual series that have removed CMC also improve obviously after partitioning. In addition, we compute displacements of these IGS stations caused by environmental loadings (including atmospheric pressure loading, non-tidal oceanic loading and hydrological loading) to analyze their contributions to the non-linear variation in GPS coordinate time series. The comparison result shows that the method we raise, PCA filtering in sub-regions, performs better than the environmental loading corrections (ELCs) in improving the signal-to-noise ratio (SNR) of GPS coordinate time series. This paper raises new criteria for selecting appropriate CMONOC stations around IGS stations when computing sub-regional CMC, involving three criteria of interstation distance, geology and self-condition of stations themselves. According to experiments, these criteria are implemental and effective in selecting suitable stations, by which to extract sub-regional CMC with higher accuracy.

Sign in / Sign up

Export Citation Format

Share Document