Key Takeaways Lithology, structure, alteration, ore/minezone, and geotechnical domaining are all built in the same implicit-modelling suites — Leapfrog Geo, Datamine Studio RM, and Vulcan — so the AI story for all five …
Key Takeaways RQD, fracture frequency, point-load strength, clay content, comminution hardness (BWI/DWI), and metal recovery (Cu, Mo, Au, Ag) can all be estimated with machine learning instead of relying purely on sparse …
Key Takeaways Both calculated mineralogical models and geometallurgical (geomet) models are, in practice, regression/classification problems trained on automated-mineralogy data — not distinct disciplines requiring separate tooling. QEMSCAN, MLA, and TIMA …
Key Takeaways Unsupervised clustering (K-means, Gaussian Mixture Models, hierarchical clustering) is now a published, credible way to define geological domains from multivariate assay/logging data. scikit-learn provides production-ready implementations of all three clustering approaches out of the …