spatio-temporal data analysis

Machine Learning-Based Spatio-Temporal Prospectivity Modeling of Porphyry Systems in the New Guinea and Solomon Islands Region

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Abstract. The discovery of new economic copper deposits is critical for the development of renewable energy infrastructure and zero-emissions transport. The majority of existing copper mines are located within current or extinct continental arc systems, but our understanding of the tectonic and geodynamic conditions favoring the formation of porphyry systems is still incomplete. Traditionally, exploration … Read more…

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

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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…