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…

Predicting the emplacement of Cordilleran porphyry copper systems using a spatio-temporal machine learning model

Porphyry copper (Cu) systems occur along magmatic belts derived in subduction zones. Our current under- standing of their formation is restricted to observations from the overriding plate, resulting in a knowledge gap in terms of processes occurring in convergence zones through time. An association between key tectonic processes and the timing and location of porphyry … Read more…