Explore mathematical geosciences, knowledge graphs, multi-source integration and spatial data analysis.
These questions guide critical reading; they are not claims about every paper listed below.
Chinese original面向人类智能与人工智能融合的矿产资源预测新范式
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 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.
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.
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.