Multi-point geostatistics for ore grade estimation

Abstract

A multi-point geostatistical method for ore grade estimation is introduced in order to fully utilize existing sampling information. A block model is used to construct a new three-dimensional training image instead of a variogram. Data events and pattern matching is improved, and the directionality of the data template is considered in the matching. The inverse distance weighted method is used to make up for the lack of multi-point geostatistics. The research improves the reliability of multi-point geostatistical estimation. Optimal estimation results for Li₂O and Ta₂O₅ come from the inverse distance weighted, ordinary Kriging, and multi-point geostatistical methods. Multi-point geostatistical estimation results are compared with those of the inverse distance weighted and ordinary Kriging methods. Deviation, trend, and variogram analyses are used to assess the effect of multipoint geostatistical estimation. This study shows that reducing the samples participating in the estimation can reduce the maximum and minimum deviation of the estimated grade to a certain extent. The grade distribution pattern is the primary factor affecting minimum and maximum deviation. This study proves the reliability and accuracy of the multi-point geostatistical method for ore grade estimation.