Improving the resource modeling results using auxiliary variables in estimation and simulation methods

Mineral resource modeling is always accompanied by challenges. It is pivotal to increase accuracy and reduce modeling errors in resource modeling. This research aims at improving the resource modeling results using auxiliary variables for estimation and simulation processes. For this purpose, the Darreh-Ziarat iron ore deposit in the west of Iran is selected as a case study. The susceptibility obtained from the 3D inversion result of the magnetometry data is used as a secondary variable in the resource modeling. First, the Fe grade was estimated by utilizing simple kriging (SK) and sequential Gaussian simulation (SGS) techniques. Then, using the auxiliary variable, the Fe grade was estimated by the cokriging (CK) and sequential Gaussian co-simulation (SGCS) methods. Considering various cut-off Fe grades, the average grade of Fe and its resource (tonnage) were calculated, and their results were compared. The mean of kriging variance saw a decline from 0.81 in the SK method to 0.67 in the CK method. This slight decrease in variance can create a profound impact on the resource classification results. The results showed that the use of an auxiliary variable in resource modeling of Darreh-Ziarat led to a reduction in estimation error, an improvement in the classification of mineral resources, and an increase in the number of high-grade Fe blocks. Finally, Fe grade values at different elevation levels were calculated using the four mentioned methods. The results revealed a strong resemblance in shallow and deep parts, while the middle part, which is the high-grade zone, showed more differences.