Substitution Random Fields with Gaussian and Gamma Distributions: Theory and Application to a Pollution Data Set |
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Authors: | Xavier Emery |
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Institution: | (1) Department of Mining Engineering, University of Chile, Avenida Tupper 2069, Santiago, Chile |
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Abstract: | This paper presents random field models with Gaussian or gamma univariate distributions and isofactorial bivariate distributions,
constructed by composing two independent random fields: a directing function with stationary Gaussian increments and a stationary
coding process with bivariate Gaussian or gamma distributions. Two variations are proposed, by considering a multivariate
directing function and a coding process with a separable covariance, or by including drift components in the directing function.
Iterative algorithms based on the Gibbs sampler allow one to condition the realizations of the substitution random fields
to a set of data, while the inference of the model parameters relies on simple tools such as indicator variograms and variograms
of different orders. A case study in polluted soil management is presented, for which a gamma model is used to quantify the
risk that pollutant concentrations over remediation units exceed a given toxicity level. Unlike the multivariate Gaussian
model, the proposed gamma model accounts for an asymmetry in the spatial correlation of the indicator functions around the
median and for a spatial clustering of high pollutant concentrations. |
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Keywords: | Conditional simulation Isofactorial bivariate distribution Bivariate Gaussian distribution Bivariate gamma distribution Gibbs sampler |
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