GEOSTATISTICAL BLOCK-SUPPORT SIMULATION FOR IRON IN TAILINGS: ADDRESSING NON-ADDITIVITY

Abstract

This study investigates the application of Sequential Gaussian Co-Simulation (SGCS) with block support to model the spatial distribution of iron (Fe) grade within a copper-gold mine tailings facility. The primary challenge addressed is the non-additive nature of iron grade, which limits the effectiveness of traditional interpolation methods that rely on linear averaging. Inaccurate modeling of such variables can lead to substantial errors in resource estimation, impacting both production planning and economic viability. To overcome this, SGCS was employed due to its capacity to incorporate secondary variables and better capture spatial variability and uncertainty. The block-support methodology was integrated to enhance the realism of the simulation by averaging sub-block realizations within larger blocks, thereby reducing interpolation error. However, block support can also suppress local extremes, which may lead to the underestimation of high values and the overestimation of low ones. In this study, density was estimated and used as a secondary variable for co-simulation due to its physical relevance and influence on Fe distribution. The simulations were conducted using Isatis.neo software, with validation performed through histogram analysis, swath plots, variogram reproduction, and cut-off tonnage curves. While the SGCS preserved spatial trends of the iron grade distribution and variogram structures effectively, the smoothing effect inherent to block support remained a limiting factor in capturing extreme values. Overall, the results demonstrate that SGCS with block support provides a robust approach for modeling non-additive variables in complex geological environments like tailings storage facilities. The findings contribute to improved geostatistical modeling approaches in resource estimation and highlight the importance of secondary variable selection, support size, and validation strategies.