Geostatistical Modeling with Histogram Uncertainty: Confirmation of a Correct Approach
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There is always uncertainty in the representative histogram required as an input for geostatistical modeling. This uncertainty should be quantified correctly and incorporated into final modeling because it affects resource/reserve estimation, investment and development decisions. This paper confirms a correct approach of quantifying histogram uncertainty by comparing to reference uncertainty found by an automated scan-based approach. The true variance of the mean is considered as the reference uncertainty. This variance is calculated by finding similar patterns of a data configuration within a large image: The mean of the specified domain is computed for each pattern to attain the true variance of the mean. The correct quantification of histogram uncertainty is defined. The spatial bootstrap provides prior uncertainty that does not consider the domain limits and is not conditioned to the data. This uncertainty is updated in geostatistical modeling to consider the limits and data. The resulting uncertainty closely matches the scan-based approach. A realistic case study is presented.
KeywordsGeostatistics Reference uncertainty Image scanning Spatial resampling Posterior uncertainty
We would like to thank two anonymous reviewers whose valuable suggestions and comments lead to improve the quality of this manuscript. We would also like to thank the sponsors of the Centre for Computational Geostatistics for financial support.
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