Magnetite Talks: Testing Machine Learning Models to Untangle Ore Deposit Classification—A Case Study in the Ossa-Morena Zone (Portugal, SW Iberia)

Abstract

A comprehensive investigation into the application of machine learning algorithms for accurately classifying mineral deposit types is presented. The study specifically focuses on iron deposits in the Portuguese Ossa-Morena Zone, employing a limited dataset of trace element geochemistry from magnetites. The research aims to derive meaningful methodological and metallogenic conclusions from the obtained results. The findings demonstrate that the combination of a restricted dataset of trace element geochemistry from magnetites with diverse machine learning models serves as a reliable tool for achieving precise classifications of mineral deposit types. Among the machine learning methods evaluated, random forest, naïve Bayes, and multinomial logistic regression emerge as the most accurate classifiers, whereas the support vector machine, the k-nearest neighbour, and artificial neural networks exhibit lower performance scores. By integrating all literature-proposed classifications, and applying them to selected iron deposits, confident classifications were obtained. Alvito and Azenhas are reliably classified as skarns, whereas Monges, Serrinha, and Vale da Arca are classified as either porphyry or a Banded Iron Formation (BIF). Notably, the classification of Orada proves cryptic, encompassing both BIF and volcanogenic massive sulphide (VMS) deposit types. Moreover, the application of machine learning models to pertinent case studies offers valuable insights not only for classifying mineral deposit types but also for discerning mixed or complex origins. This approach provides meaningful results that can aid in the interpretation of mineral deposit types and may facilitate the identification of new mineral exploration targets. The research highlights the robustness of machine learning algorithms in interpreting magnetite data and underscores their potential significance in exploration projects.