Abstract
Abstract Geologic modeling is an important step in determining the benefits and final pit dimensions for mining operations. Geostatistical models and distance-based functions are the main methods used to estimate the grade behavior. However, these two methods, despite their similar mean values, differ in spatial variability. The objective of this article is to prove, by comparing the two methodologies, that models with different spatial variability using the Lerchs-Grossmann algorithm will output subtly different final pit dimensions and scheduling. Furthermore, with the direct block schedule (DBS), these differences can be considerable. The tests compared the methodologies using the following three models: inverse distance (ID), ordinary kriging (OK) and turning bands simulation (TBS). The results demonstrate that the Lerchs-Grossmann algorithm is only slightly sensitive to the spatial variability of the grade; however, DBS requires the model populations to be better defined because of its greater sensitivity to spatial variability.
Highlights
Determining the lithology and grade distribution in a deposit is highly relevant to the mineral industry
The spatial distribution of the samples directly influences the estimation of resources and reserves (NERY, 1995)
The present study evaluated the impacts caused by different methods for obtaining the block model on mine planning steps by employing the deterministic method via LG and stochastic simulation in direct block schedule (DBS)
Summary
Determining the lithology and grade distribution in a deposit is highly relevant to the mineral industry. The spatial distribution of the samples directly influences the estimation of resources and reserves (NERY, 1995). - Supporting effect: the estimation process is completed by generating blocks with grade distribution, with separate estimations for each block of the model. The definition of the block dimensions has a great influence on the estimation since the definition is performed based on information obtained by drill holes with much smaller dimensions than the estimated block. This situation requires the definition of the best block dimension to ensure the reliability of the resource estimation for the data (NERY, 1995)
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