https://youtu.be/OY0A1FZ7nUs

This groundbreaking research introduces a novel data-knowledge dual-driven model that combines artificial intelligence with mineral systems approach for mineral prospectivity mapping. The study focuses on Fe polymetallic deposits in southwestern Fujian Province, China, integrating geological, geochemical, and geophysical data through advanced machine learning techniques including Graph Convolutional Neural Networks (GCNs) and Long Short-Term Memory (LSTM) networks. The model's architecture innovatively incorporates both data-driven learning and geological knowledge constraints, allowing for more accurate identification of potential mineral deposits. The research demonstrates that by using appropriate node thresholds in heterogeneous graphs, the model can effectively characterize mineral system components and their interactions, leading to improved prediction performance in identifying prospective areas for Fe deposits. P. Geo. Ricardo A Valls, M. Sc. and Geo Gadfly Valls Geoconsultant ORCID ID- https://orcid.org/0000-0002-5421-0914 Scopus Author ID: 7003369619/35335510700 ResearcherID: S-6604-2018 If you like this content, please "buy me a coffee" https://www.buymeacoffee.com/goldendroplets