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

He, Yuefan; Nie, Guigen; Wu, Shu‐Fang Vivienne; Li, Haiyang

doi:10.3390/rs13030431

Cited by 9 publications

(4 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the time length increased to 12 years, the percentage and the amplitude of RW increased, indicating that the long-period components of the noise, such as RW noise, became significant in GNSS time series. In the vertical component, the influence of the noise model is more significant than that in the other two components [50][51][52]. As the time period increased, the percentage of the FNWN model decreased, and the percentages of the GGMWN and PLWN models increased.…”

Section: Gnss Time Span On Noise Model Estimationmentioning

confidence: 96%

The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Sun

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Accurate noise model identification for GNSS time series is crucial for obtaining a reliable GNSS velocity field and its uncertainty for various studies in geodynamics and geodesy. Here, by comprehensively considering time span and missing data effect on the noise model of GNSS time series, we used four combined noise models to analyze the duration of the time series (ranging from 2 to 24 years) and the data gap (between 2% and 30%) effects on noise model selection and velocity estimation at 72 GNSS stations spanning from 1992 to 2022 in global region together with simulated data. Our results show that the selected noise model have better convergence when GNSS time series is getting longer. With longer time series, the GNSS velocity uncertainty estimation with different data gaps is more homogenous to a certain order of magnitude. When the GNSS time series length is less than 8 years, it shows that the flicker noise and random walk noise and white noise (FNRWWN), flicker noise and white noise (FNWN), and power law noise and white noise (PLWN) models are wrongly estimated as a Gauss–Markov and white noise (GGMWN) model, which can affect the accuracy of GNSS velocity estimated from GNSS time series. When the GNSS time series length is more than 12 years, the RW noise components are most likely to be detected. As the duration increases, the impact of RW on velocity uncertainty decreases. Finally, we show that the selection of the stochastic noise model and velocity estimation are reliable for a time series with a minimum duration of 12 years.

show abstract

Section: Gnss Time Span On Noise Model Estimationmentioning

confidence: 96%

The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Sun

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…In addition, the use of LDCmgm90 shows a significant improvement to most stations for both data-trend-removed and the data-trend-retained cases. He et al [3] analyzed and discussed the optimal noise model characteristics of global GNSS time series data of 671 IGS reference stations from 2000 to 2021. He et al [3] analyzed different noise model combinations and computed the optimal noise model for each station before and after hydrologic loading correction.…”

Section: Improvement Of Current Geodetic Measurement Techniquesmentioning

confidence: 99%

“…He et al [3] analyzed and discussed the optimal noise model characteristics of global GNSS time series data of 671 IGS reference stations from 2000 to 2021. He et al [3] analyzed different noise model combinations and computed the optimal noise model for each station before and after hydrologic loading correction. The results show that optimal noise model characteristics of most stations can be classified into three main categories: white noise + flicker noise, generalized Gauss-Markov noise and white noise + power law noise.…”

Section: Improvement Of Current Geodetic Measurement Techniquesmentioning

confidence: 99%

Geodetic Monitoring for Land Deformation

Liu

Chang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…As the time length increased to 12 years, the percentage and the amplitude of RW increased, indicating that the long-period components of the noise, such as RW noise, became significant in GNSS time series. In the vertical component, the influence of the noise model is more significant than that in the other two components[42][43][44].As the time period increased, the percentage of the FNWN model decreased, and the percentages of the GGMWN and PLWN models increased. When the time length is 12 years, the detection rates of the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 23 June 2023 doi:10.20944/preprints202306.1667.v1…”

mentioning

confidence: 96%

Impact of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Sun¹,

Lu²,

Hu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Noise model selection criteria has a significant impact on identifying the stochastic noise proper-ties of any GNSS daily coordinate time series. The low-frequency random walk noise existing in these time series could lead to overestimation of the tectonic rate, so it is of great significance to accurately detect the random walk component. This study focuses on noise model estimation cri-terion (BIC_tp) derived from the AIC and the BIC by introducing 2π factors. It is more sensitive to abnormal steps (random jumps). Using observation data from 72 GNSS stations from 1992 to 2022 and simulated data, four combined noise models are used to explore the impacts of time se-ries lengths (ranging from2 to 24 years) and data loss (between 2% and 30%) on noise models and velocity estimation. The results show that as the time length increased, the selected optimal noise model, and the estimated uncertainty of the tectonic trend with different data gap, gradually con-verge. When the time length is short (less than 8 years), it could lead to the FNRWWN, FNWN, and PLWN models being mistakenly estimated as GGMWN models, thereby affecting the accura-cy of determining the station velocity parameters. When the time length is 12 years, the RW noise component is more probably detected, As the time length increases, the impact of RW on velocity uncertainty is weakened. Finally, we conclude that for a time series with a minimum time length of 12 years, both the selection of the optimal stochastic noise model and the estimation of the ve-locity parameters are reliable.

show abstract

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

Cited by 9 publications

References 41 publications

The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Geodetic Monitoring for Land Deformation

Impact of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series

Contact Info

Product

Resources

About