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dc.contributor.authorDarbeheshti, Neda
dc.contributor.authorFeatherstone, Will
dc.identifier.citationDarbeheshti, Neda and Featherstone, Will. 2009. Non-stationary covariance function modelling in 2D least-squares collocation. Journal of Geodesy. 83 (6): pp. 495-508.

Standard least-squares collocation (LSC) assumes 2D stationarity and 3D isotropy, and relies on a covariance function to account for spatial dependence in the ob-served data. However, the assumption that the spatial dependence is constant through-out the region of interest may sometimes be violated. Assuming a stationary covariance structure can result in over-smoothing of, e.g., the gravity field in mountains and under-smoothing in great plains. We introduce the kernel convolution method from spatial statistics for non-stationary covariance structures, and demonstrate its advantage fordealing with non-stationarity in geodetic data. We then compared stationary and non-stationary covariance functions in 2D LSC to the empirical example of gravity anomaly interpolation near the Darling Fault, Western Australia, where the field is anisotropic and non-stationary. The results with non-stationary covariance functions are better than standard LSC in terms of formal errors and cross-validation against data not used in the interpolation, demonstrating that the use of non-stationary covariance functions can improve upon standard (stationary) LSC.

dc.publisherSpringer - Verlag
dc.subjectgravity field interpolation
dc.subjectelliptical kernel convolution
dc.subjectnon-stationary covariance function
dc.subjectDarling Fault
dc.subjectLeast squares collocation (LSC)
dc.titleNon-stationary covariance function modelling in 2D least-squares collocation
dc.typeJournal Article
dcterms.source.titleJournal of Geodesy

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curtin.accessStatusOpen access
curtin.facultyDepartment of Spatial Sciences
curtin.facultyFaculty of Science and Engineering
curtin.facultyWA School of Mines

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