Predictive Mapping of Prospectivity for Gold in the Central Portion of the Tapajós Mineral Province, Brazil

Abstract

This work aims to model mineral prospectivity for intrusion–related gold deposits in the central portion of the Tapajós Mineral Province (TMP), southwestern Pará state. The scope includes experimentation and evaluation of knowledge and data-driven methods applied to multisource data to predict potential targets for gold mineralization. The radiometric data processing allowed to identify a hydrothermal alteration footprint of known gold deposits, providing information in regions with little or no field data available. The aeromagnetic data analysis prompted the identification of high magnetic zones, which are probably related to hydrothermal fluid transport. Linear features extracted from digital elevation data revealed an NNW–SSE general trend, which is consistent with the main structural control of deposits. The data were integrated through three modeling techniques—fuzzy logic (knowledge-driven), weights of evidence (WofE, data-driven), and a machine learning algorithm (SVM, data-driven)—resulting in three prospective models. In all models, the majority of indicated prospective regions coincide with the known deposits. The results obtained in the models were combined to generate an agreement map, which mapped the overlapping of their highest prospective scores, indicating new areas of prospective interest in the central portion of the TMP.