Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabeled machine learning

Porphyry copper deposits contain the majority of the world’s discovered mineable reserves of copper. While these deposits are known to form in magmatic arcs along subduction zones, the precise contributions of different factors in the subducting and overriding plates to this process are not well constrained, making predictive prospectivity mapping difficult. Empirical machine learning-based approaches … Read more…

Keynote Talk at Exploration in the House: Critical minerals – prospectivity mapping using generative AI

In the recent Exploration in the House event at Parliament House in Sydney Dietmar provided an overview of the use of generative AI for assessing copper, nickel and cobalt prospectivity in the Lachlan fold belt, based on the Honours thesis of Nathan Wake, and work by Ehsan Farahbakhsh and Vera Nolte-Wilson. The event also featured … Read more…