Key Takeaways Alteration modelling has quietly shifted from “geologist draws domains from logging” to “hyperspectral data informs the domains directly.” Leapfrog Geo’s implicit modelling engine (FastRBF) is the standard tool for building the 3D alteration surfaces …
Key Takeaways Clay content is a first-order driver of flotation, filtration, and tailings behavior — getting it wrong in the block model has real downstream cost. Corescan’s HCI and CSIRO’s HyLogger are the two dominant systems for measuring clay mineralogy from core at scale. …
Key Takeaways “Dynamic” block models update automatically as new drilling or production data comes in, instead of sitting static until the next scheduled model rebuild. This is a named, shipping feature in commercial mine-planning suites — not a research concept — specifically in …
Key Takeaways Brownfield exploration (drilling near an existing mine) already sits on top of dense historical data — drillholes, block models, grade control records — which is exactly the fuel AI targeting tools need. There’s no separate “brownfield AI” product category; teams …
Key Takeaways Corescan and CSIRO’s HyLogger are the two systems that dominate this space — there isn’t a fragmented field of competitors to evaluate. CSIRO’s MyLogger is the concrete “neural network on spectra” step that makes …
Key Takeaways A proxy model is a classic supervised-regression problem: predict an expensive/slow-to-measure variable from cheap/fast-to-measure ones. Cancha, built specifically for geometallurgy, and general-purpose Python (scikit-learn, XGBoost) are the two realistic paths — commercial turnkey vs. …
Key Takeaways Predicting copper recovery and ore hardness from routine measurements is one of the best-documented ML applications in geometallurgy right now (2024-2026 case studies). Gradient-boosted tree models — XGBoost, LightGBM, Random Forest via …