Spatiotemporal noise in GPS position time-series from Crustal Movement Observation Network of China

Wei, Wensheng; Qiao, Xuejun; Wang, Dijin; Chen, Zhengsong; Yu, Pengfei; Lin, Mingsen; Chen, Wei

doi:10.1093/gji/ggy506

Cited by 13 publications

(8 citation statements)

References 39 publications

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“…Generally, the WN version has the highest SNRs for each component, and the Filtered version has the higher SNRs than the Unfiltered version, detailed in Table 3. If the average of SNR is taken as an indicator to evaluate the influence of CME and noise model on the velocity estimation, the averaged SNR of the WN version is about 10 dB larger than that of the Unfiltered version, while that of the Filtered version is about 5 dB larger than the Unfiltered version; it is not surprising that the WN version has the largest SNRs since velocity uncertainties have been found underestimated 8-10 times in previous studies [42,43]. As suggested by these studies, it is not proper to estimate the velocity field just based on WN only model and the coloured noise should be considered.…”

Section: The Influence Of Cme and Noise Model On The Estimation Of Cr...mentioning

confidence: 93%

“…Some signals cause spatial responses to change smoothly with obvious boundaries between positive and negative responses, such as IC5, IC7 and IC8 of the east component; IC1, IC4, IC5 and IC10 of the up component. These signals may be related to some seasonal and geographic factors, such as monument types and thermal expansions [42]. Some signals have strong responses in local areas, especially in the up component, such as the Tianshan region and Sichuan-Yunnan region (circled by dotted lines) in IC3, the Tianshan region and the southeastern costal region in IC6, and North China block and South China block regions in IC9.…”

Section: Cme Extractionmentioning

confidence: 99%

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Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis

et al. 2022

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Continuous Global Positioning Systems (GPS) coordinate time series with a high spatiotemporal resolution, and provide a great opportunity to study their noise models and common mode errors (CMEs), thus making it possible to detect and analyse spatiotemporal characteristics of tectonic and non-tectonic signals in time series, and further to estimate a reliable and accurate velocity field of crustal movement in a region by removing CMEs and using the optimal noise model. In this paper, we used GPS coordinate time series from the Crustal Movement Observation Network of China (CMONOC) with an approximate decadal period from 2010 to 2020, to construct optimal noise models by fitting them with several noise combinations according to the Akaike information criterion (AIC). We further adopted independent component analysis (ICA) to extract CMEs and analysed their spatiotemporal characteristics, and then evaluated their effects on noise models and velocity uncertainties, and finally estimated a decennial velocity field of crustal movement with a higher signal-to-noise ratio (SNR) by applying the CME filtering and considering the optimal noise model in Mainland China. Our results show that optimal noise models are dominated by white noise (WN) plus flicker noise (FN) for both east and north components, and WN plus power law noise (PN) with spectral index close to −1 for up component, respectively. ICA filtering can remove the highly spatially correlated CMEs and decrease the mean RMSEs of the residual time series by about 40–60%, providing a more accurate velocity field with a higher SNR in Mainland China, accordingly.

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Section: The Influence Of Cme and Noise Model On The Estimation Of Cr...mentioning

confidence: 93%

Section: Cme Extractionmentioning

confidence: 99%

Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis

et al. 2022

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“…Figure 7f shows an oscillation with a period of approximately one year. As we have removed the signal with a period of one yr from the GPS time series by using the least squares method before employing PCA, and Figure 7g shows that the frequency of this signal is approximately 1.03 cpy, we suggest that a potential interpretation of this frequency is attributed to the GPS draconic error related to the satellite orbit [36][37][38][39]. Figure 7f also clearly shows a signal with a period of ~6 yr, and the corresponding Fourier spectrum in Figure 7e also confirms this finding.…”

Section: Wavelet and Fourier Spectra Of Common Mode Componentsmentioning

confidence: 99%

“…GPS time series may contain seasonal, interannual, unmodeled signals, and noises, which may be explained by functional and stochastic noise models [39]. Therefore, a stochastic model that accounts for temporal correlations in the GPS time series should be incorporated to identify realistic geophysical information.…”

Section: Nonlinear Signals Of An Unmodeled Cmcmentioning

confidence: 99%

Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau

et al. 2019

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Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.

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“…The remaining stations are expressed as band pass+powerlaw noise (BPPL) and first-order Gauss-Markov+random walk noise (FOGMRW). In 2019, Wang et al [21] analyzed the coordinate time series of 260 continuous GPS stations in the Crustal Movement Observation Network of China (CMONOC). In the noise evaluation, the influence of periodic signals is considered, and the maximum likelihood estimation method is used to discuss the noise characteristics of the residual time series that remove seasonal signals and the coordinate time series that further remove other periodic signals.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading

Nie

et al. 2021

Remote Sensing

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The displacement of Global Navigation Satellite System (GNSS) station contains the information of surface elastic deformation caused by the variation of land water reserves. This paper selects the long-term coordinate series data of 671 International GNSS Service (IGS) reference stations distributed globally under the framework of World Geodetic System 1984 (WGS84) from 2000 to 2021. Different noise model combinations are used for noise analysis, and the optimal noise model for each station before and after hydrologic loading correction is calculated. The results show that the noise models of global IGS reference stations are diverse, and each component has different optimal noise model characteristics, mainly white noise + flicker noise (WN+FN), generalized Gauss–Markov noise (GGM) and white noise + power law noise (WN+PL). Through specific analysis between the optimal noise model and the time series velocity of the station, it is found that the maximum influence value of the vertical velocity can reach 1.8 mm when hydrological loading is considered. Different complex noise models also have a certain influence on the linear velocity and velocity uncertainty of the station. Among them, the influence of white noise + random walking noise is relatively obvious, and its maximum influence value in the elevation direction can reach over 2 mm/year. When studying the impact of hydrological loading correction on the periodicity of the coordinate series, it is concluded whether the hydrological loading is calculated or not, and the GNSS long-term coordinate series has obvious annual and semi-annual amplitude changes, which are most obvious in the vertical direction, up to 16.48 mm.

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Spatiotemporal noise in GPS position time-series from Crustal Movement Observation Network of China

Cited by 13 publications

References 39 publications

Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis

Spatiotemporal Filtering for Continuous GPS Coordinate Time Series in Mainland China by Using Independent Component Analysis

Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau

Analysis and Discussion on the Optimal Noise Model of Global GNSS Long-Term Coordinate Series Considering Hydrological Loading

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