Sun, X.; Lu, T.; Hu, S.; Huang, J.; He, X.; Montillet, J.-P.; Ma, X.; Huang, Z. The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series. Remote Sens.2023, 15, 3572.
Sun, X.; Lu, T.; Hu, S.; Huang, J.; He, X.; Montillet, J.-P.; Ma, X.; Huang, Z. The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series. Remote Sens. 2023, 15, 3572.
Sun, X.; Lu, T.; Hu, S.; Huang, J.; He, X.; Montillet, J.-P.; Ma, X.; Huang, Z. The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series. Remote Sens.2023, 15, 3572.
Sun, X.; Lu, T.; Hu, S.; Huang, J.; He, X.; Montillet, J.-P.; Ma, X.; Huang, Z. The Relationship of Time Span and Missing Data on the Noise Model Estimation of GNSS Time Series. Remote Sens. 2023, 15, 3572.
Abstract
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.
Keywords
GNSS time series; time length; missing data; noise analysis; velocity estimation
Subject
Environmental and Earth Sciences, Space and Planetary Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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