Abstract

Cokriging has been applied to estimate the distribution of moisture within a rock pile of low-grade gold ore, or heap. Along with the primary data set of gravimetric moisture content obtained from drilling, electrical resistivity was used to supplement the estimation procedure by supplying a secondary data set. The effectiveness of the cokriging method was determined by comparing the results to kriging the moisture data alone and through least-squares regression (LSR) modeling of colocated resistivity and moisture. In general, the wells from which moisture data were derived were separated by distances far greater than the horizontal correlation scale. The kriging results showed that regions generally undersampled by drilling reverted to the mean of the moisture data. The LSR technique, which provides a simpletransformation of resistivity to moisture, converted the low resis-tivity to highmoisture, and vice versa. The sparse well locations created a high degree of uncertainty in the transformed data set. Extreme resistivity values produced nonphysical moisture values, either negative for the linear model or values greater than one for the power model. The cokriging application, which considers the correlation scale and secondary data, produced the best results, as indicated through the cross validation. The mean and variance of the cokriged moisture were closer to the measured moisture, and the bias in the residuals was the lowest. The application likely could be improved through optimal well placement, whereby the resistivity results guide the drilling program through gross target characterization, and the moisture estimation could be updated iteratively.

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