Contributions of computing in geosciences: a case study of geometallurgical forecasting

Geometallurgical modeling has been improved over the years with the incorporation of mineralogical, chemical, and metallurgical information. However, the diversity of the database brings challenges since the information is not unified in procedures, sample support, or quantities. Characteristics such as nonlinearity and nonadditivity of the geometallurgical database impose even more difficulties. The objective of this article is to demonstrate the feasibility of geometallurgical models from multivariate techniques combined with computational tools. The algorithms used in this study were developed in Python programming language. The multivariate analysis and machine learning techniques allowed the grouping of transitional typological domains and the quantification of uncertainty; the selection and reduction of the dimensionality of the heterotopic database; and the prediction of metallurgical behavior from exhaustive primary variables using a transfer function. The results were verified and proved to be satisfactory. Other benefits of the computational structure were: reduction of subjectivity; automation; and dynamic updates of models using new data.