Combination of geostatistical simulation and fractal modeling for mineral resource classification

Abstract

The separation, identification and assessment of high-grade ore zones from low-grade ones are extremely important in mining of metalliferous deposits. A technique that provides reliable results for those purposes is thus paramount to mining engineers and geologists. In this paper, the simulated size–number (SS–N) fractal model, which is an extension of the number–size (N–S) fractal model, was utilized for classification of parts of the Zaghia iron deposit, located near Bafq City in Central Iran, based on borehole data. We applied this model to the output of the turning bands simulation method using the data, and the results were compared with those of the application of the concentration–volume (C–V) fractal model to the output of kriging of the data. The technique using the SS–N model combined with turning bands simulation presents more reliable results compared to technique using the C–V model combined with kriging since the former does not present smoothing effects. The grade variability was classified in each mineralized zones defined by the SS–N and C–V models, based on which tonnage cut-off models were generated. The tonnage cut-off obtained using the technique of combining turning bands simulation and SS–N modeling is more reliable than that obtained using the technique of combining kriging and C–V modeling.