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

Wu, Shu‐Fang Vivienne; Nie, Guigen; Liu, Jingnan; Kezhi, Wang; Chen, Xue; Wang, Jing; Li, Haiyang; Peng, Fengyou; Ren, Xiaobing

doi:10.3390/rs11111389

Cited by 6 publications

(5 citation statements)

References 29 publications

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“…Moreover, we would like to emphasise the importance of the choice of the GNSS network for performing PCA [40]. The choice of a homogeneous network is wise in order to equally distribute the signal across the entire region.…”

Section: Common Mode Estimation In Gnssmentioning

confidence: 99%

“…Finally, in order to search for the true geophysical signals in interannual variations, the extraction of spatial common modes from the signal is usually carried out [37]. This can be conducted using different techniques: principal component analysis (PCA) [38][39][40], independent component analysis (ICA) [41], or robust statistical methods [42]. Since the sources are not necessarily independent, it is reasonable to choose PCA in order to compare the interannual signal from GNSS with the signals derived from hydrological models, GRACE (Gravity Recovery and Climate Experiment), and the GRACE Follow-On time variable gravity field [43].…”

Section: Introductionmentioning

confidence: 99%

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Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models

et al. 2021

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Thanks to the increasing number of permanent GNSS stations in Europe and their long records, we computed position solutions for more than 1000 stations over the last two decades using the REPRO3 orbit and clock products from the IGS CNES-CLS (GRGS) Analysis Center. The velocities, which are mainly due to tectonics and glacial isostatic adjustment (GIA), and the annual solar cycle have been estimated using weighted least squares. The interannual variations have been accounted for in the stochastic model or in the deterministic model. We demonstrated that the velocity and annual cycle, in addition to their uncertainties, depend on the estimation method we used and that the estimation of GPS draconitic oscillations minimises biases in the estimation of annual solar cycle displacements. The annual solar cycle extracted from GPS has been compared with that from loading estimates of several hydrological models. If the annual amplitudes between GPS and hydrological models match, the phases of the loading models were typically in advance of about 1 month compared to GPS. Predictions of displacements modelled from GRACE observations did not show this phase shift. We also found important discrepancies at the interannual frequency band between GNSS, loading estimates derived from GRACE, and hydrological models using principal component analysis (PCA) decomposition. These discrepancies revealed that GNSS position variations in the interannual band cannot be systematically interpreted as a geophysical signal and should instead be interpreted in terms of autocorrelated noise.

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Section: Common Mode Estimation In Gnssmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models

et al. 2021

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“…There are few studies on the extraction and removal of CME at large spatial scales [20][21][22], and these focused only on the effect of different filtering methods for large spatial scales. Related studies have shown that the spatial responses (SRs) of PCA/ICA to partial versus whole principal/independent components differ significantly across the study regions [23][24][25][26][27], with small spatial scales of the region filtering results being better than those of large spatial scales [28]. Delineating subregions exponentially increases the workload, but there is no uniform delineation standard.…”

Section: Introductionmentioning

confidence: 99%

CMONOC II Common Mode Error Analysis and Structural Region Division Research

Bai,

Xiao,

Miao

et al. 2024

Remote Sensing

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The modern tectonic deformation of the Chinese mainland is dominated by landmass movements, and active tectonic block regions are geological units with a relatively uniform movement pattern. The removal of CMEs can provide more accurate GPS data for exploring the movement characteristics between active tectonic block regions. In order to improve the effect of CME extraction, we propose that the Crustal Movement Observation Network of China be divided into sub-regions based on the refined definition of active tectonic block regions of the Chinese mainland. In this paper, 247 stations in the CMONOC II network are used to form a large spatial scale GPS network and 6 sub-regions with small spatial scale GPS networks. For the large spatial scale GPS network, we compare and analyze the effects of PCA and ICA filtering, and the study shows that PCA is not suitable for CME extraction in this large spatial scale GPS network, while ICA filtering is better. Subsequently, the large spatial scale GPS network and six small spatial scale GPS networks were used to extract CME using ICA, with the results showing that the RMSE values of the residual time series of the large spatial scale GPS network were reduced by 9.60%, 17.08%, and 16.14% in the directions of E, N, and U, respectively, and that the subregions divided according to the refined and determined first-level active plots of the Chinese continent had their residual time series of the RMSE values were reduced by 26.19%, 26.95%, and 28.32% on average in the three respective directions of E, N, and U. The effect of extracting CMEs by dividing the subregions was 29.16%, 5.44%, and 39.84% higher than the effect of extracting them as a whole in the three directions of E, N, and U, respectively. The experimental results demonstrate that the CMONOC II observation network is an effective and feasible method to extract CMEs according to the finely defined active tectonic block region of the Chinese mainland at the first level.

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“…A seismic jump is a co-seismic slip and its following motion is a post-seismic slip. To model a post-seismic decay motion, exponential function [Nikolaidis, 2004], logarithmic function [Bevis and Brown, 2014;Itoh and Nishimura, 2016;Wu et al, 2019] or a combination of them [Tobita, 2016;Klos et al, 2019] can be used.…”

Section: Introductionmentioning

confidence: 99%

“…Two usual methods to study the spatial correlation are the stacking method [Wdowinski et al, 1997;Nikolaidis, 2004;Marquez-Azua and De Mets, 2003;Wdowinski et al, 2004;Wang et al, 2012;Jiang et al, 2018] and the Empirical Orthogonal Function (EOF) method [Teferle et al, 2008]. In the branch of the EOF method, the Principal Component Analysis (PCA) method [Dong et al, 2006;He et al, 2015;Liu et al, 2015;Borghi et al, 2016;Gruszczynski et al, 2018;Birhanu et al, 2018;Wu et al, 2019;Tan et al, 2020] and the Karhunen-Loeve Expansion (KLE) method [Dong et al, 2006] can be mentioned. In the stacking method, the Common Mode Error (CME) parameter is calculated to remove the spatial correlation between the stations.…”

Section: Introductionmentioning

confidence: 99%

A time series analysis of permanent GNSS stations in the northwest network of Iran

Khalkhali

Ardalan

Karimi

2021

Annals of Geophysics

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The aim of this study is to estimate reliable velocities along with their realistic uncertainties based on a robust time series analysis including analysis of deterministic and stochastic (noise) models. In the deterministic model analysis part, we use a complete station motion model comprised of jump effects, linear and nonlinear trend, periodic components, and post-seismic deformation model. This part also consists of jump detection, outlier detection, and statistical significance of jumps. We perform the deterministic model analysis in an iterative process to elevate its efficiency. In the noise analysis part, first, we remove the spatial correlation of observations using the weighted stacking method based on the common mode error (CME) parameter. Next, a combination of white and flicker noises is used to determine the stochastic model. This time series analysis is applied for 11-year time series of 25 permanent GNSS stations from 2006 to 2016 in the northwest network of Iran. We reveal that there is a nonlinear trend in some stations, although most stations have a linear trend. In addition, we found that a combination of logarithmic and exponential functions is the most appropriate post-seismic deformation model in our study region. The result of the noise analysis shows that the spatial filtering reduces the norm of post-fit residual vector by 19.34%, 17.51%, and 12.44% on average for the east, north, and up components, respectively. Furthermore, the uncertainties obtained from the combination of white and flicker noises at the east, north, and up components are 5.0, 4.8, and 4.4 times greater than those of the white noise model, respectively. The results indicate that the stations move horizontally with an average velocity of 36.0 ± 0.3 mm/yr in the azimuth of 52.66° NE which is compatible with velocities obtained from MIDAS. We obtained the vertical velocity of most stations in the range of -5 to 5 mm/yr. However, in three stations of GGSH, ORYH, and BNAB, which are in the proximity of Lake Urmia, the vertical velocities are estimated to be -80.9 mm/yr, -50.6 mm/yr, and -11.4 mm/yr, respectively. Moreover, we found that these three stations possess large periodic signal amplitudes in all three coordinate components as well as a nonlinear trend in the up component.

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

Cited by 6 publications

References 29 publications

Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models

Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models

CMONOC II Common Mode Error Analysis and Structural Region Division Research

A time series analysis of permanent GNSS stations in the northwest network of Iran

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