Statistical treatment of outlier data in epithermal gold deposit reserve estimation

Abstract

A common characteristic of gold deposits is highly skewed frequency distributions. Lognormal and three-parameter lognormal distributions have worked well for Witwatersrand-type deposits. Epithermal gold deposits show evidence of multiple pulses of mineralization, which make fitting simple distribution models difficult. A new approach is proposed which consists of the following steps: (1) ordering the data in descending order. (2) Finding the cumulative coefficient of variation for each datum. Look for the quantile where there is a sudden acceleration of the cumulative C.V. Typically, the quantile will be above 0.85. (3) Fitting a lognormal model to the data above that quantile. Establish the mean above the quantile, Z H * . This is done by fitting a single or double truncated lognormal model. (4) Use variograms to establish the spatial continuity of below-quantile data (ZL) and indicator variable (1 if below quantile, 0 if above). (5) Estimate grade of blocks by (1*) (Z L * )+(1 − 1*) (Z H * ), where 1* is the kriged estimate of the indicator, and Z L * is the kriged estimate of the below quantile portion of the distribution. The method is illustrated for caldera, Carlin-type, and hot springs-type deposits. For the latter two types, slight variants of the above steps are developed.