Paper Info
Title
Adaptive Least-Squares Collocation Algorithm Considering Distance Scale Factor for GPS Crustal Velocity Field Fitting and Estimation
Abstract
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.
Year
DOI
Venue
2019
10.3390/rs11222692
REMOTE SENSING
Keywords
DocType
Volume
GPS velocity field,Least-squares collocation algorithm (LSC),Distance scale factor,Adaptive adjustment,fitting and estimation
Journal
11
Issue
Citations 
PageRank 
22
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Wei Qu100.34
Hailu Chen200.34
Shichuan Liang300.34
Qin Zhang41613.46
Lihua Zhao500.34
Yuan Gao600.68
Wu Zhu702.03