Machine Learning Pinpoints Hidden Gold Zones in Ghana’s Beposo District

In the gold-rich heart of Ghana’s Ashanti Region, a team of geoscientists has shown that artificial intelligence can do something remarkable: take nothing but airborne magnetic and radiometric measurements, learn from just 67 known gold occurrences, and draw a map that captures the overwhelming majority of those occurrences inside a small fraction of the landscape. The study, led by Eric Dominic Forson of the University of Ghana and published in Earth Science Informatics, applied two machine learning classifiers—a random forest and a decision tree—to the Beposo area, a roughly 706 square kilometre patch of the Paleoproterozoic Birimian Supergroup where systematic mineral prospectivity mapping has, until now, been strikingly limited despite the region’s celebrated orogenic gold endowment.

The work matters because the Birimian terrane of West Africa is one of the world’s great gold provinces, hosting world-class deposits such as Obuasi on the Ashanti Belt, yet many parts of it remain under-explored in a modern, quantitative sense. Rather than letting an algorithm sift raw data blindly, the researchers anchored their modelling in a mineral-systems approach. This framework, widely used in contemporary exploration targeting, asks explorers to translate what is known about how a deposit type forms—its sources of fluid and metal, the pathways that move those fluids, the structural traps where gold precipitates, and the alteration haloes left behind—into explicit, testable expectations. At Beposo, the documented structural and hydrothermal controls on gold mineralisation were converted into a set of geoscientific predictor layers against which the model outputs could be judged.

Those predictors were derived exclusively from airborne geophysical data: eight layers extracted from magnetic and radiometric surveys supplied by the Ghana Geological Survey Authority. Airborne magnetics reveals patterns of magnetisation in the crust, exposing faults, shear zones, lithological boundaries and intrusive bodies even beneath tropical soils and vegetation. Radiometric data, which measure gamma-ray emissions from potassium, thorium and uranium in the near-surface, serve as a proxy for hydrothermal alteration—because fluids that deposit gold typically also redistribute potassium and other elements in the wall rocks around a vein system. Crucially, the team did not feed geochemistry into the models as predictors; geochemical data were used solely to label the 67 known gold occurrences as positive training examples, a design choice that keeps the model honest and makes the resulting maps genuinely predictive rather than merely descriptive.

Two classifiers were trained and compared. The decision tree, a long-established technique dating back to the 1980s, makes predictions by recursively splitting the data according to simple rules, producing a transparent, tree-like logic that geologists can inspect. The random forest, introduced by Leo Breiman in 2001, builds hundreds of decision trees on random subsets of the data and predictors, then averages their votes. This ensemble strategy typically suppresses the overfitting to which single trees are prone and yields more stable probability estimates. For both models, the hyperparameters—the internal settings that govern how aggressively a model learns—were optimised using a 10-fold GridSearchCV procedure within a spatially independent training partition, a detail that matters because spatial autocorrelation in geological data can otherwise inflate apparent performance when neighbouring points leak between training and validation sets.

Evaluation was carried out on an identical held-out test set using a battery of complementary metrics: receiver operating characteristic and precision–recall curves, standard classification scores, and the Brier score, which penalises overconfident wrong probabilities. The random forest model achieved an area under the ROC curve of 0.93, an F1-score of 0.85 and a Brier score of 0.134. The decision tree trailed with an AUC of 0.85, an F1-score of 0.81 and a Brier score of 0.152. An AUC of 0.93 means that if one known gold occurrence and one randomly chosen non-occurrence location were drawn from the map, the model would rank the true occurrence higher 93 percent of the time—a strong result for prospectivity mapping, where the geological signal is notoriously subtle and the number of known deposits is small.

The authors are careful, however, not to overclaim. Because the held-out test set is small, the bootstrap confidence intervals of the two models overlap, meaning the random forest cannot be declared statistically superior to the decision tree. It is reported instead as retaining the higher central estimate on every metric. This kind of statistical candour is refreshing in a field where machine learning headlines often outrun the evidence, and it reflects a broader maturation of prospectivity mapping as a discipline: the question is no longer only whether an algorithm can fit the training data, but whether its probabilities are calibrated, its validation spatially honest, and its conclusions robust to the small sample sizes that inevitably characterise frontier exploration areas.

One of the most scientifically interesting findings concerns which predictors the models deemed important. Lineament density—a measure of how densely fractured and faulted the bedrock is, as expressed in the magnetic data—ranked as the single most influential predictor in both models. This is a direct quantitative endorsement of the structural-control hypothesis: orogenic gold in the Birimian is emplaced where crustal-scale shear zones and their subsidiary fractures channelled gold-bearing hydrothermal fluids into sites of pressure drop and chemical reaction. Yet when the predictors were grouped thematically, the structural-magnetic group and the hydrothermal-alteration group contributed comparably overall, suggesting that neither structure nor alteration alone tells the whole story. Gold accumulates where permeable structures intersect chemically reactive ground, and the models appear to have learned exactly that conjunction.

The practical payoff is captured in the maps’ ability to concentrate known gold into small, actionable areas. The high-prospectivity class of the random forest model captured 92.5 percent of the 67 known occurrences within just 159 square kilometres—22.52 percent of the study area. The decision tree’s high-prospectivity zone captured 80.6 percent of occurrences over 91 square kilometres, or 12.89 percent of the area. In exploration terms, this is the entire point of prospectivity mapping: a company or geological survey with finite drilling budgets can focus follow-up work on the delineated high-prospectivity zones, which the authors describe as providing a defensible basis for prioritising exploration at Beposo. The fact that the random forest achieves its higher capture rate over a larger area, while the decision tree is more aggressive in its discrimination, illustrates a genuine trade-off between sensitivity and specificity that explorers must weigh against their own risk tolerance.

Beyond the immediate results, the study’s methodological template may prove its most durable contribution. The workflow—derive a mineral-systems conceptual model, translate it into geophysically derived predictors, label occurrences with independent geochemistry, optimise hyperparameters under spatially independent validation, and evaluate with calibrated probabilistic metrics—offers a framework that is potentially transferable to analogous Birimian terranes across Ghana and the wider West African Craton, from the Sefwi belt to the greenstone belts of Burkina Faso and Côte d’Ivoire. The authors themselves note that this transferability remains a hypothesis to be tested on independent datasets, an appropriately cautious framing. Still, in a period when the global mining industry is scrambling to replace depleting reserves and machine learning is increasingly touted as the future of exploration, the Beposo study provides something rarer than hype: a carefully validated, statistically transparent demonstration that decades-old airborne geophysical surveys, a sound geological concept, and well-implemented classifiers can converge on the hidden architecture of gold mineralisation.

Subject of Research: Machine learning-based gold prospectivity mapping using airborne geophysical data in the Birimian Supergroup of Ghana

Article Title: Structurally controlled gold in the Beposo area, Ashanti Region, Ghana: testing a mineral-systems targeting model with random forest and decision tree classifiers

Article References: Forson, E. D., Nunoo, S., Amponsah, P. O., Osei, R. D., & Sakyi, P. A. (2026). Structurally controlled gold in the Beposo area, Ashanti Region, Ghana: testing a mineral-systems targeting model with random forest and decision tree classifiers. Earth Science Informatics, 19(10), Article 171. https://doi.org/10.1007/s12145-026-02221-8

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02221-8

Keywords: gold, Ghana, Birimian Supergroup, machine learning, random forest, decision tree, mineral prospectivity mapping, airborne geophysics, orogenic gold, Ashanti Region, exploration targeting, structural geology