Application of Multiple-Point Statistics (MPS) for Stochastic Gold Grade Estimation in Areas with Sparsely Spaced Drillhole Data

The application of multiple point statistics (MPS) in oil studies has gained traction over the years; however, the use of MPS in mineral resource estimation and classification is still limited. This is despite MPS’ ability to deal with complex and heterogeneous geology better than the commonly applied two-point based methods. The availability and choice of a representative training image (TI) are often cited as the main challenge holding back MPS’ application in mining geostatistical studies. On the other hand, the general approach used in mining whereby early focus is on higher grade areas and lower grades are planned to be mined later presents a massive opportunity for the development of representative TIs. Mined-out areas provide large amounts of historical data that can be used to develop training images or training datasets that can be used to estimate in mining extension areas (often lower in grade), provided low grade areas are underpinned by similar geology analogue. This approach will enable more robust estimates in mine/pit extension areas for both high- and low-grade extensions than with traditional two-point geostatistical methods.