ASSESSING THE FEASIBILITY OF GEOSTATISTICAL APPROACHES TO QUANTIFY STOCKPILES CHARACTERIZATION USING VARIOGRAM MODELING (WITH CASE EXAMPLES FROM TWO IRON STOCKPILES, IRAN)

Structural analysis of stockpiles variability is a crucial subject in stockpiles exploitations. Many environmental companies have developed and commercialized a proprietary technology to remediate tailings and stockpiles areas - harmless to both humans and the environment-resulting from mining operations. Recently modern processing and recovery methods and also increasing the metal demands with low grades lead to implementation of the remaining vast stockpiles as renewable resources. These large above ground stockpiles can be quantified, graded and valued to produce reliable modeling of processing costs and expected revenues. So, in many cases modeling stockpiles variability is a fundamental component of a financial model in mining and environmental projects. Although there is a high volume of stockpiles almost in all mine sites, there is not much work on geostatistical characterization of internal distribution of stock pile parameters. According to the geostatistical approaches in reserve estimation, it should be considered that the natural mineralization trend may not exist in the stockpiles. The main problem is the mixed nature of such variability, partly natural and partly artificial. However, the sequential time and location pilings from the main deposit can lead to a similar natural structure in its piles. Hence, variogram modeling of stockpiles parameters can consider as a beneficial tool to interpret the stockpiles variability in environmental applications. In this paper, geostatistical structural analysis and possibility of using a Kriging method to estimate of stockpiles parameters is explored. Two large stockpiles are tested by interpreting their experimental variograms. The estimation of Fe and P was approached for High phosphorous and low grade stockpiles at Choghart iron mine, Iran. Explaining the stationary characterization in piles is compared with the geostatistical factors of the main deposit using blast holes data from excavation levels of Choghart iron mine. To identify the structural characterization, the preparing database, separating statistical homogenous regions, trend analysis, variogram modeling and its interpretations is applied. This innovation may help other stocks to consider the feasibility of geostatistical approaches in their variability studies and can be consider as a step forward in environmental applications.