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Pareto–Lognormal Modeling of Known and Unknown Metal Resources

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An Erratum to this article was published on 30 September 2016

Abstract

Recently, large worldwide databases with statistics on amounts of metal in mineral deposits have become available. Frequently, most metal is contained in the largest deposits for a metal. A major problem in meaningful modeling of the size–frequency distributions of the largest deposits is that they are very rare. Until now it was rather difficult to establish the exact form of their size–frequency distribution. However, because of the new very large databases it can now be concluded that two commonly used approaches (lognormal and Pareto) thought to be mutually incompatible in the past, are both correct with a high probability. One approach does not necessarily exclude validity of the other. Patiño-Douce (Nat Resour Res 25(1):97–124, 2016b) has shown that metal tonnage frequency distributions for worldwide metal deposits are approximately lognormal with similar standard deviations (σ) of log-transformed data. In this paper, it is assumed that worldwide metals satisfy both lognormal and Pareto models simultaneously. Copper and Au are taken for example for comparison with results previously obtained for these two metals in the Abitibi area of the Canadian Shield. Worldwide there are 2541 Cu deposits approximately satisfying a lognormal distribution. Total amount of Cu in these deposits is 2.319 × 109 tons of Cu. However, the 45 largest deposits, which together contain 1.281 × 109 tons of Cu, satisfy a Pareto distribution. If their lognormal model would apply in the upper tail as well, these 45 largest deposits should have contained only about 0.076 × 109 tons of Cu. It is shown in detail for Cu that the best statistical model for Cu deposits is a worldwide Pareto–lognormal model in which the basic lognormal size–frequency distribution is flanked by two juxtaposed Pareto distributions for the largest and smallest Cu deposits, respectively. Both Pareto distributions smoothly change into the central lognormal by means of bridge functions that can be determined separately. The worldwide Pareto–lognormal model also was found to be applicable to several other metals, especially Ag, Ni, Pb, and U. For Au, the model does not work as well for the upper tail Pareto distribution as it does for the other metals taken for example.

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Acknowledgments

Thanks are due to Alberto Patiño-Douce and an anonymous reviewer for constructive comments that have helped to improve the text.

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  1. Geological Survey of Canada, 601 Booth Street, Ottawa, ON, K1A 0E8, Canada

    Frits Agterberg

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  1. Frits Agterberg
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Correspondence to Frits Agterberg.

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An erratum to this article is available at http://dx.doi.org/10.1007/s11053-016-9312-5.

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Agterberg, F. Pareto–Lognormal Modeling of Known and Unknown Metal Resources. Nat Resour Res 26, 3–20 (2017). https://doi.org/10.1007/s11053-016-9305-4

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