Abstract
Ore resource modelling is an essential aspect of mining operation. It is also a crucial pre-mining step required for a successful exploitation of mineral deposits. Ajabanoko iron ore resource model was developed and the ore reserve estimate carried out using inverse distance method as contained in Surpac 6.4.1 mine software. The total number of blocks used for the model is 54,475. Ore estimation result obtained from thirteen drill hole data indicates 38,313,595 tonnes of iron ore and density of 3.65 tonnes/m3. The average grade and total volume of the ore body is 36.36 % and 10,496,595 m3 respectively. Â
Highlights
The application of computer software packages in recent times to ore resource modelling has greatly improved the economic evaluation of mineral resources
An understanding of the geology of a deposit is fundamental to the Mineral Resource Estimation (MRE) process, since estimates are constrained by the geological complexity captured within the geological model, usually based on geology, geometry, structural nature, and grade distribution of the deposit [3]
The block model summary of the ore body is presented in Table 4, while the density for Ajabanoko iron ore deposit is 3.65 tonnes /m3
Summary
The application of computer software packages in recent times to ore resource modelling has greatly improved the economic evaluation of mineral resources. State-of-the-art software uses algorithms that sequentially divide each block into smaller blocks in order to follow the geological structures (sub-blocking), but this approach does not achieve the level of precision required and results in models with an extremely high number of blocks [9]. Most of these methods are contained in widely available commercial mine design softwares and makes ore resource modelling relatively easier to accomplish. The objectives of the study are to develop a resource model for Ajabanoko iron ore deposit and subsequently estimate the ore reserve
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