Uncertainty in the Global Mean for Improved Geostatistical Modeling

Abstract

Analysis of uncertainty in ore reserves impacts investment decisions, mine planning and sampling. Uncertainty is evaluated by geostatistical simulation and is affected by the amount of data and the modeling parameters. Incomplete uncertainty is given because the parameter uncertainty is ignored. Also, greater spatial continuity leads to more uncertainty. This increase is unreasonable in earth science. To address these problems, two approaches are proposed. The first approach is based on multiGaussian simulation where many realizations are performed at translated and/or rotated configurations and conditioned to the data. Variable configurations give different mean values that define uncertainty. The second approach is based on a stochastic trend; this approach randomizes the trend coefficients accounting for the fitted coefficients correlation. Variable set of coefficients provide different mean values. Furthermore, a methodology to account for parameter uncertainty is proposed. The uncertainty in the mean is transferred through simulation to deliver a more complete uncertainty.