Machine learning framework for prospectivity mapping of Cu–Au porphyry mineralisation in the Lachlan Fold Belt, eastern Australia
Year: 2026
Type: article
Abstract: • Machine learning-based framework predicts porphyry mineralisation with high accuracy in NSW, Australia. • Positive-Unlabelled Bagging and Random Forest address limited data challenges in mineral exploration. • The model captures 95% of known mineral occurrences in 5% of the study area. • Diverse geological, geophysical, and remote sensing datasets are integrated for precise prospectivity mapping. • Greenfield zones and prioritised targets are delineated in the porphyry-rich Lachlan Fold Belt within NSW. The growing demand for critical minerals in green technologies has increased the need to discover deep deposits. This study uses machine learning-based mineral prospectivity mapping to predict Cu–Au porphyry mineralisation in the Lachlan Fold Belt, New South Wales, Australia. Using a positive-unlabelled bagging approach with a random forest classifier, the framework addresses challenges like limited negative samples and complex exploration datasets. Incorporating geological, geophysical, and remote sensing data with deposit size-based weighting, the framework generates precise maps of mineral prospectivity. High-potential zones in the porphyry-rich Lachlan Fold Belt, were identified and ranked into four exploration priorities based on key geological features. The model captures 95% of known mineral occurrences within just 5% of the study area, demonstrating high accuracy and efficiency. Additionally, the model identifies the most important data layers and features for the modelling process. Among them, four particularly important features stand out: the first vertical derivative of total magnetic intensity that reveals subsurface magnetic variations linked to mineralisation; distance to faulted boundaries that identify pathways for mineralising fluids; the thorium-to-potassium ratio indicating alteration processes related to ore formation; and distance to intrusive boundaries reflecting the role of magmatic events in porphyry systems. These features played a significant role in discriminating between mineralised and non-mineralised areas. The generated prospectivity map reveals a clear spatial correlation between areas of high probability and known mineral occurrences while also highlighting several potential greenfield zones for further exploration.
Source: Journal of Asian Earth Sciences
Authors: Elnaz Heidari, Ehsan Farahbakhsh, Hojat Shirmard, Fabian Kohlmann, Phillip Blevin, R Dietmar Müller
Institution: The University of Sydney
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,330
Funder: Australian Research Council
OpenAlex ID: W7128729282