| Popis: |
Mineral chemistry analysis is a valuable tool in several phases of mineralogy and mineral prospecting studies. This type of analysis can point out relevant information, such as concentration of the chemical element of interest in the analyzed phase and, thus, the predisposition of an area for a given commodity. Due to this, considerable amount of data has been generated, especially with the use of electron probe micro-analyzers (EPMA), either in research for academic purposes or in a typical prospecting campaign in the mineral industry. We have identified an efficiency gap when manually processing and analyzing mineral chemistry data, and thus, we envisage this research niche could benefit from the versatility brought by machine learning algorithms. In this paper, we present Qmin, an application that assists in increasing the efficiency of mineral chemistry data processing and analysis stages through automated routines. Our code benefits from a hierarchical structure of classifiers and regressors trained by a Random Forest algorithm developed on a filtered training database extracted from the GEOROC (Geochemistry of Rocks of the Oceans and Continents) repository, maintained by the Max Planck Institute for Chemistry. To test the robustness of our application, we applied a blind test with more than 11,000 mineral chemistry analyses compiled for diamond prospecting within the scope of the Diamante Brasil Project of the Geological Survey of Brazil. The blind test yielded a balanced classifier accuracy of ca. 99% for the minerals known by Qmin. Therefore, we highlight the potential of machine learning techniques in assisting the processing and analysis of mineral chemistry data. |
