Welcome to this hands-on video tutorial on Gold Prospectivity Mapping! Learn how to build and evaluate predictive models using Random Forest (RF) and XGBoost by fusing high-resolution aeromagnetic and radiometric survey data with known gold occurrences.

Taking this video tutorial will give you a competitive edge in modern mineral exploration by teaching you how to transform raw geophysical data into accurate target maps. You will also master the entire Python-driven machine learning pipeline—from engineering magnetic and radiometric features to optimizing Random Forest and XGBoost models. Ultimately, you will gain the practical skills needed to reduce drill-targeting risk, make data-backed exploration decisions, and produce publication-ready spatial outputs used by exploration companies worldwide.

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Yusuf A. Musa

(ymusa.phy@buk.edu.ng)

Determination of Crustal Thickness and Stability Using Oasis Montaj + GM-SYS