Non-stationary variogram models for geostatistical sampling optimisation:an empirical investigation using elevation data

Atkinson, Peter M. and LLoyd, Christopher D. (2007) Non-stationary variogram models for geostatistical sampling optimisation:an empirical investigation using elevation data. Computers and Geosciences, 33 (10). pp. 1285-1300. ISSN 0098-3004

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Abstract

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.

Item Type:
Journal Article
Journal or Publication Title:
Computers and Geosciences
Additional Information:
M1 - 10
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1710
Subjects:
ID Code:
77246
Deposited By:
Deposited On:
18 Dec 2015 16:04
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jul 2020 02:29