In geometallurgy, a process model operating at the mineral liberation level needs quantitative textural information about the ore. The utilization of this information within process modeling and simulation will increase the quality of the predictions.

In this study, descriptors derived from color images and machine learning algorithms are used to group drill core intervals into textural classes and estimate mineral maps by automatic pixel classification. Different descriptors and classifiers are compared, based on their accuracy and capacity to be automated. Integration of the classifier approach with mineral processing simulation is also demonstrated. The quantification of textural information for mineral processing simulation introduced new tools towards an integrated information flow from the drill cores to a geometallurgical model.

The approach has been verified by comparing traditional geological texture classification against the one obtained from automatic methods. The tested drill cores are sampled from a porphyry copper deposit located in Northern Sweden.