Deep Time Data Mining
Interrogating plate reconstructions to target mineral deposits using AI
Mapping craton edges to find critical metals
Our new paper in Geoscience Frontiers uses full-waveform seismic tomography and machine learning to map craton boundaries worldwide — and shows that 85% of target mineral deposits …
Craton boundary detection from full-waveform tomography model reveals links to critical metal deposits
Using full-waveform seismic tomography to detect craton boundaries and their spatial links to critical metal deposit locations.
Seafloor scars point to where copper deposits form
Our paper in International Geology Review shows that fracture zones, seamounts, and large igneous provinces on the subducting ocean floor promote porphyry copper formation — …
Subducting seafloor anomalies promote porphyry copper formation
Plate tectonic reconstructions and machine learning show that subducting seafloor anomalies promote porphyry copper deposit formation.
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.
