Orebody modeling is critical for the evaluation and engineering of mineral deposits. The building of the 3D geometry is conventionally based on vertical and horizontal sections interpreted by a mine geologist. In more advanced cases, geostatistical methods are used such as indicator kriging and/or simulations, truncated gaussian or plurigaussian simulations, which allows to automate the modeling process. These methods are probabilistic and use the variogram (so called two point statistics) to represent the geological heterogeneity.
Multiple point geostatistics (MPG) is an alternative to traditional variogram-based geostatistical modeling, whereas a fully explicit representation of the geological patterns (a training image) is used in place of variograms. Although it is now routinely used in modeling of oil and gas reservoirs, there are few studies showing application of this technique in mineral deposits. The advantages of the MPG approach are to provide a more realistic representation of the geology through a more accessible parametrization (the visual training image instead of the analytic variogram).
This paper presents initial results of MPG with the SNESIM algorithm (single normal equations simulation) applied to multiple lithological domains at a Brazilian iron ore deposit. Additionally, the steps involved in dataset preparation for adequate use of the algorithm are discussed.