It is recognised by the current JORC (1999) code that resource classification involves the interaction of numerous qualitative and quantitative criteria such as data quality, geological continuity and grade continuity. No prescribed criteria and rules will work for all situations or even between different ore types within the same deposit. Also these criteria and rules are sometimes applied without a clear understanding of their appropriateness, accuracy or correct implementation. For these reasons case studies are useful to evaluate and compare criteria commonly used to assist in resource classification. In this paper blasthole data from a selected area of Joffre Member hosted ore of the Brockman Iron Formation at the Mt Whaleback orebody are used as the basis of a case study for the above-mentioned purposes._x000D_
Iron grade, in per cent, is interpolated from a blasthole dataset into a block model using ordinary kriging. Samples are then removed from this blasthole dataset to produce a random sample grid, a semi-random sample grid and a regular sample grid. Using nearest neighbour, inverse distance squared, ordinary kriging and sequential gaussian simulation, block iron grades are estimated using these three blasthole data subsets as inputs. This provides four groups of estimates with the ordinary kriging estimate based on the complete blasthole dataset being considered to represent the true estimate of iron grades. Also generated during this process are estimates of the error for each block. These error estimates range from the simple, such as drill spacing, through to more advanced methodologies such as kriging efficiency and simulation. These estimates of grade and grade estimation error are then compared to the true estimate using graphs and statistics._x000D_
In order of increasing accuracy the block grade estimation and simulation methods were nearest neighbour, inverse distance weighting, ordinary kriging and finally conditional simulation. The simulation in some instances doubled the accuracy of individual block grade estimates when compared to nearest neighbour and inverse distance weighting estimates. It was found that many methodologies for estimating the error of individual blocks performed equally well with only methods such as average drill hole spacing and classification by search ellipse pass number performing poorly. An approach on how to convert the abovementioned error estimates of individual blocks into a meaningful JORC classification is also discussed._x000D_
Although advanced non-linear resource estimates are applicable, most iron ore mines are still using relatively straightforward methods. The use of blasthole data and some simple linear interpolation methods and simple linear based error estimates makes this study repeatable for most iron ore sites and their resource geologists. This style of investigation is recommended as a useful approach for the Competent Person to apply to their deposit and thus better select, implement and understand the criteria used for resource classification and provide more consistency and confidence in the resource classification process.