Application of geostatistics in Datamine software for the estimation of ore grades of a metallic deposit. (#1826)

Authors

Vilchez Calla, Erlita Mariceli

Dávila Huamán, Frank Del Piero

Abstract

This project applies geostatistical techniques, such as ordinary Kriging and the inverse distance method, using Datamine software to estimate the ore grades of a metal deposit and optimize the Optimal Mining Plan (OMP). The research is applied, with a descriptive approach detailing the characteristics and distribution of ore grades in the deposit, without manipulating variables. Geostatistical analyses, including copper and arsenic variograms, have allowed an accurate interpretation of their spatial distribution, improving the evaluation of the project's profitability. The distribution of 8,711 arsenic samples shows low concentrations with some significant anomalies, while copper shows moderate concentrations. The variograms of copper (0.027) and arsenic (3.1) facilitate the understanding of their behavior and variability, which contributes to optimize the decision making for mining. The detailed design of the mine, including the pit, access ramps, dump and roads, ensures an efficient and profitable operation. The applied geostatistical methodology allows for accurate ore grade estimates, which optimizes the design and execution of the mining operation, minimizing risks and maximizing project profitability. In conclusion, the use of Datamine software and geostatistics has been essential to the success of grade estimation, mine planning and mineral resource optimization, allowing for a more efficient and profitable utilization of the deposit.