Key Takeaways Acid-base accounting (ABA) itself is a standard, decades-old static/kinetic lab test — there’s no AI in the test protocol. PHREEQC, the free USGS geochemical modeling engine, is the standard open-source tool for extending raw ABA results into predictive geochemical models. The …
Key Takeaways “Advanced sensing” on the grade-control floor is really an umbrella for three mature field/lab instruments: portable XRF/XRD, hyperspectral core scanning, and LIBS. None of these are AI tools by themselves — the AI value-add comes from the software layer that turns their …
Key Takeaways TIMA, MLA, and QEMSCAN are all SEM-based automated mineralogy platforms — they scan a polished sample, fire an electron beam, and classify every mineral grain from its X-ray spectrum, at rates up to ~100,000 grains/hour. All three now lean on …
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 MineMax by NTWIST is explicitly AI-driven — it sits as a supervisory layer over existing mine and plant systems, continuously learning from operational outcomes to make real-time blend and feed recommendations. Deswik Blend, BOLT, and GO are the …
Key Takeaways Airborne total magnetic intensity (TMI), gravity, and magnetometry survey processing is classical geophysics — Geosoft Oasis montaj (Seequent) remains the industry-standard processing software. The AI layer is additive, not a replacement: …
Key Takeaways Choosing and interpreting geochemical analytical methods (aqua regia vs. multi-acid digestion, ICP-MS vs. XRF, etc.) is increasingly software-assisted rather than purely analyst judgment. ioGAS (IMDEX) is the industry-standard platform for exploring and interpreting multivariate …
Key Takeaways Bench slope design still runs on classical finite-element and limit-equilibrium software — RocScience’s Slide2/RS2/RS3 and GeoStudio remain the industry standard. The AI layer is emerging and upstream of the design software: machine-learning models predicting rock-mass properties …
Key Takeaways Throughput, recovery, flotation kinetics, and concentrate grade can each be modeled as a “soft sensor” — an ML model that continuously predicts plant performance from live sensor/historian data instead of a static simulation. …
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. …