Given the enormous volume of the collected data for geological mapping and mineral exploration, it is essential to employ tools suitable to process the data altogether to achieve a fully integrated interpretation. Machine Learning (ML) techniques have been widely used in numerous fields to provide data-related solutions for complex problems. There has been an increasing interest in application of ML in geoscience to help mineral potential mapping and geological mapping. However, complexity and uncertainly associated with geological events differentiate geoscience from other fields of ML applications. It is paramount to frame the geological question to be solved by ML in a realistic manner. Such an approach will help to choose what type of data should be used to feed the model and what to expect from the model output.
The main objective of this work is to train a model using available exploratory data and generate maps that represent prospective areas for gold and base metal exploration.