Objective mapping of Yilgarn granites

Recently completed MRIWA project M10587 applied advanced machine learning techniques to integrate large geophysical datasets and produce the first consistent, craton-scale classification of granites across the entire Yilgarn Craton. This study identified new geological patterns, refined terrane boundaries, and revealed hidden structures beneath cover. 

This improved understanding of Yilgarn geology could help target future exploration for granite-hosted commodities including gold, lithium and rare earth elements, reducing risk, attracting investment, and supporting more sustainable exploration. 

For more details, read MRIWA report 10587 summarising the findings of this research.