Abstract

Characterization of spatial variability in earth science commonly requires random fields which are stationary within delineated domains. This contribution presents an alternative approach for simulating attributes in combination with a non-stationary first-order moment. A new procedure is presented to unambiguously decompose the observed behaviour into a deterministic trend and a stochastic residual, while explicitly controlling the modelled uncertainty. The practicality of the approach resides in a straightforward and objective inference of the variogram model and neighborhood parameters. This method does not require a prior removal of the trend. The inference principle is based on minimizing the deviation between empirical and theoretical errors calculated for increasingly distant neighborhood shells. Further, the inference is integrated into a systematic simulation framework and accompanying validation guidelines are formulated. The effort results in a characterization of the resource uncertainty of an existing heavy mineral sand deposit.

Highlights

  • The development of a mineral resource is inherently connected with a substantial amount of risk due to large financial investments required at times when geological knowledge is rather limited

  • The practicality of the proposed method resides in a straightforward and objective inference of model parameters, which does not require a prior removal of the complex trend

  • The inference method is further integrated into a systematic simulation framework based on general least squares (GLS) and sequential Gaussian simulation

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Summary

Introduction

The development of a mineral resource is inherently connected with a substantial amount of risk due to large financial investments required at times when geological knowledge is rather limited. A misfit could result in a severe bias during the assessment of uncertainty. Common estimation algorithms such as Universal or Dual Kriging (UK/DK) can be applied to model both components simultaneously (Isaaks and Srivastava 1989; Goovaerts 1997; Webster and Oliver 2007). DK can be used to decompose the observed attribute into a deterministic trend and a stochastic residual. These algorithms require that the underlying covariance function of the residuals is known a priori. Since the trend model results from the solution of the kriging equations, which requires a covariance model

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