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.
CITATION STYLE
Olea, R. A. (2018). Advances in sensitivity analysis of uncertainty to changes in sampling density when modeling spatially correlated attributes. In Handbook of Mathematical Geosciences: Fifty Years of IAMG (pp. 375–393). Springer International Publishing. https://doi.org/10.1007/978-3-319-78999-6_19
Mendeley helps you to discover research relevant for your work.