Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes

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Abstract

A comparative analysis of distance methods, kriging and stochastic simulation is conducted for evaluating their capabilities for predicting fluctuations in uncertainty due to changes in spatially correlated samples. It is concluded that distance methods lack the most basic capabilities to assess reliability despite their wide acceptance. In contrast, kriging and stochastic simulation offer significant improvements by considering probabilistic formulations that provide a basis on which uncertainty can be estimated in a way consistent with practices widely accepted in risk analysis. Additionally, using real thickness data of a coal bed, it is confirmed once more that stochastic simulation outperforms kriging.

Additional publication details

Publication type Book chapter
Publication Subtype Book Chapter
Title Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes
DOI 10.1007/978-3-319-78999-6_19
Year Published 2018
Language English
Publisher Springer
Contributing office(s) Eastern Energy Resources Science Center
Description 19 p.
First page 375
Last page 393