Adaptive Least-Squares Collocation Algorithm Considering Distance Scale Factor for GPS Crustal Velocity Field Fitting and Estimation

High-precision, high-reliability, and high-density GPS crustal velocity are extremely important requirements for geodynamic analysis. The least-squares collocation algorithm (LSC) has unique advantages over crustal movement models to overcome observation errors in GPS data and the sparseness and poo...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2019-11, Vol.11 (22), p.2692
Main Authors: Qu, Wei, Chen, Hailu, Liang, Shichuan, Zhang, Qin, Zhao, Lihua, Gao, Yuan, Zhu, Wu
Format: Article
Language:English
Subjects:
Accuracy
adaptive adjustment
Adaptive algorithms
Algorithms
Collocation
Coordinate transformations
Covariance
distance scale factor
fitting and estimation
Global positioning systems
GPS
gps velocity field
Least squares
least-squares collocation algorithm (lsc)
Noise
Random signals
Reliability aspects
Spatial data
Statistical analysis
Statistics
Systematic errors
Tectonics
Velocity
Velocity distribution
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Summary:High-precision, high-reliability, and high-density GPS crustal velocity are extremely important requirements for geodynamic analysis. The least-squares collocation algorithm (LSC) has unique advantages over crustal movement models to overcome observation errors in GPS data and the sparseness and poor geometric distribution in GPS observations. However, traditional LSC algorithms often encounter negative covariance statistics, and thus, calculating statistical Gaussian covariance function based on the selected distance interval leads to inaccurate estimation of the correlation between the random signals. An unreliable Gaussian statistical covariance function also leads to inconsistency in observation noise and signal variance. In this study, we present an improved LSC algorithm that takes into account the combination of distance scale factor and adaptive adjustment to overcome these problems. The rationality and practicability of the new algorithm was verified by using GPS observations. Results show that the new algorithm introduces the distance scale factor, which effectively weakens the influence of systematic errors by improving the function model. The new algorithm can better reflect the characteristics of GPS crustal movement, which can provide valuable basic data for use in the analysis of regional tectonic dynamics using GPS observations.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs11222692