ABSTRACT
Working with different data sets in the mineral resource estimation is a common challenge to be addressed by the industry. Sampling methods, sensor devices, measurement times along the ROM and key variables measured might differ between data sets. These variations are reflected in the quality of each data set. Comparative exploratory data analysis is used to verify if different data sets are sampling the same distribution. Frequently, they show differences in the statistics and variography. This demonstrates that different sets cannot just be merged and used in processes if these are not previously treated. One way of integrating is to attribute a measurement error to the unreliable data set. The methodology proposed enables a resources estimation and risk analysis by considering the variance of measurement error calculated from the cokriging between reliable and unreliable data sets. This paper illustrates an innovative methodology with an application to a sulphide deposit.
