A Geostatistical Contribution to Assess the Risk Embedded in Resource Classification Methods

It is recognised by the major international codes that resource classification involves the interaction of numerous qualitative and quantitative criteria such as data quality, geological and grade continuity, etc. However, the difficulty in quantifying the degree of uncertainty associated with mineral resources estimation has led to the creation of a large suite of methodologies, terms and definitions, with almost every mining company having its own set of standards. It is also intuitively known that several traditionally used methods to evaluate resources do not take into account the spatial continuity of the grades, being unable to provide a reliable measure of the error associated with their estimates. Since they do not provide an error assessment, these methods are inappropriate to assess local or global uncertainty associated with an estimate. Faced with these problems, this work aims at developing a methodology for either quantitative or qualitative analysis of mineral resources estimation, through incorporation of uncertainty and the correct definition of the associated risk or error. The proposed methodology includes the development of a software code incorporating all the main classification techniques used by the mineral industry. A comprehensive study about each technique was conducted allowing a comparison of the parameters affecting mineral inventory assessment. A case study was conducted with a typical iron ore deposit data set from the Conceitpo mine of the Itabira Complex in the Iron Quadrangle, Brazil. The results showed the specific limitations of each classification system and the influence in selecting their key parameters, always based on subjective choices.