Subducting seafloor anomalies promote porphyry copper formation
Plate tectonic reconstructions and machine learning show that subducting seafloor anomalies promote porphyry copper deposit formation.
Christopher P. Alfonso
PhD Graduate, EarthByte Group
Christopher Alfonso completed his PhD in Geophysics at the University of Sydney, researching the use of spatio-temporal data mining and machine learning to understand the formation of major mineral deposits, particularly porphyry copper systems.
Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabeled machine learning
A positive-unlabeled machine learning model predicts spatio-temporal copper prospectivity along the American Cordillera from subduction zone dynamics.
Kimberlite eruptions driven by slab flux and subduction angle
Machine learning reveals that the volume and angle of subducted oceanic plates control where and when diamond-bearing kimberlite eruptions occur.
Deep time spatio-temporal data analysis using pyGPlates with PlateTectonicTools and GPlately
GPlately: a Python interface for deep-time spatio-temporal data analysis using pyGPlates, simplifying plate tectonic reconstructions.