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Jef Caers
Stanford UniversityMeeting roles in:
Automated Geophysical Pattern Recognition for Mineral Exploration: Case Study of Carajás, Brazil, Using IOCG Deposits
Beyond Grade and Tonnage: Sequential AI Planning for Mineral Exploration and Responsible Mining
Integrating Multi-Source Geospatial Data for Data-Driven Predictive Modeling of Rare Earth Element Concentrations in Penco, Chile
Layer-Resolved Prediction of Subsurface Mineralization from Surface Soil Geochemistry Using Feature Sensitivity and Similarity Learning
MultiGrid simulation: artifact-free stochastic interpolation of flightline data accounting for locally-varying anisotropy
From geological hypothesis to AI: enhancing mineral exploration decisions by quantifying geological uncertainty
General Surrogate Model Based on Neural Network for Large-scale Stochastic Electromagnetic Inversion
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