Key Takeaways First-pass (“quick”) geological logging — the initial lithology/alteration read on fresh core — is one of the more mature AI use cases in exploration. Datarock Core and Seequent Central’s logging module both support AI-accelerated first-pass logging directly from core …
Key Takeaways “Real-time geometallurgy” is the umbrella term for fusing ore characterization proxies (grind/crush hardness proxies, mineral liberation, %solids) into live setpoint recommendations for the plant, rather than waiting for periodic lab testwork. It runs on the same …
Key Takeaways MineSense ShovelSense mounts high-speed XRF sensors on shovel/excavator buckets to scan material for grade in real time, bucket by bucket. The system’s ML models are trained per ore body, so grade estimates adapt to your specific …
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 …
Key Takeaways Model refinement — recalibrating a groundwater model against real monitoring data — runs on PEST++, the open-source parameter-estimation and uncertainty-analysis suite that works with MODFLOW 6 or FEFLOW. PEST++ handles model-independent (non-intrusive) calibration, meaning it …
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 …
Key Takeaways Photogrammetric structural mapping — extracting discontinuity orientation, spacing, and trace length from pit-wall imagery — is the established standard, via Maptek’s I-Site Studio/PointStudio and the CSIRO-developed Sirovision system. Automated discontinuity-detection algorithms …
Key Takeaways SWIR (short-wave infrared, roughly 1,300-2,500nm) hyperspectral scanning reads mineralogical signatures that visible-light imaging simply can’t see — especially clay and alteration mineral fingerprints. Corescan’s Hyperspectral Core Imager and Malvern Panalytical’s …
Key Takeaways Tailings dam failure prediction has moved from periodic manual survey to continuous AI-driven monitoring — the key breakthrough is deep-learning models that separate normal consolidation settlement from precursor shear deformation (the actual warning sign) in InSAR satellite data. …
Key Takeaways Tailings dam monitoring has become its own specialized AI category following several high-profile dam failures. GroundProbe (Orica) combines radar (including its SSR-SARx synthetic-aperture radar built specifically for tailings) with piezometers and drone imagery in one dashboard. …