AI summaryProposes a workflow that embeds geological domain knowledge in input data, model design and output interpretation to address interpretability, generalisation and physical consistency in AI-based mineral prospectivity mapping.
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AI summaryTrains machine-learning models on zircon chemistry to distinguish tectonic settings and magma types, then applies them to Hadean zircons from Jack Hills. The results provide clues to early crust formation and subduction-related sediment recycling.
AI summaryReviews the development of mineral resource prediction and discusses combining geological knowledge, big data and AI. Examples from covered areas, deep exploration and porphyry copper knowledge graphs illustrate linked-system modelling and human–AI collaboration.
AI mineral explorationMineral predictionNonlinear theoryKnowledge graphs
AI summaryIntegrates prior geological knowledge into an adversarial autoencoder to identify geochemical anomalies associated with tungsten mineralisation in southern Jiangxi. Multifractal singularity analysis quantifies relationships between ore-controlling factors and known deposits to inform the model.