This cluster of papers focuses on the application of machine learning, remote sensing, and compositional data analysis techniques for mineral prospectivity mapping. It explores the use of advanced technologies such as ASTER and hyperspectral imaging to identify geological features, geochemical anomalies, and hydrothermal alterations associated with mineralization. The cluster also delves into the challenges and opportunities in using support vector machines, fractal modeling, and statistical analysis for predicting undiscovered mineral deposits.