Categories on Eigenform AI Geo Tooltips

Concentrate Handling, Smelting & Refining

Mon, 21 Sep 2026 00:00:00 +0800

Concentrate handling, smelting and refining is a smaller AI-adoption category on this site so far, covering AI-assisted chemical analysis of concentrate composition and the transport and shipping logistics of getting concentrate from mine to smelter.

Both are earlier-stage applications than exploration or flotation, but the underlying pattern is the same: replace a periodic manual check with a continuous, calibrated automated one.

Crushing & Grinding

Mon, 21 Sep 2026 00:00:00 +0800

Crushing and grinding is a comminution-circuit optimization problem at its core: predicting how ore texture and grain size will behave through the circuit, then using that to tune crushing and grinding setpoints in real time rather than on a fixed schedule.

This category covers the image-analysis tools that measure texture and grain size upstream, and the real-time geometallurgy models that turn that measurement into an actual circuit-optimization decision on the plant floor.

Drilling & Blasting Operations / Blasthole Analysis

Mon, 21 Sep 2026 00:00:00 +0800

Drilling and blasting generate a stream of sensor data - measurement-while-drilling logs, blast-hole probe readings, hydrogeologic monitoring feeds - that AI increasingly processes in real time rather than after the fact. This covers autonomous and AI-assisted drill rigs, blast-hole logging tools like IMDEX’s BLASTDOG, and real-time ore-grade sensing at the shovel.

It also covers the groundwater and hydrological models that inform dewatering and blast design, and the hydrogeologic monitoring systems that keep a pit safe to work in. The common thread across this group is turning continuous field instrumentation into decisions an operator can act on during the shift, not in next week’s report.

Exploration & Target Generation

Mon, 21 Sep 2026 00:00:00 +0800

Exploration and target generation is where most day-one AI adoption happens in mining, because the data - drill core, geochemistry, geophysics surveys - already exists in the volumes machine learning needs. This is the instrument layer: automated mineralogy platforms like TIMA, MLA and QEMSCAN, hyperspectral core scanners for clay and alteration mapping, portable XRF and LIBS analyzers, and airborne gravity and magnetics surveys.

On top of that instrument layer sits a growing set of prediction models: rock strength from point-load testing, comminution parameters, and the density and geochemical proxies that feed a resource model long before mining starts. If you’re deciding where to start applying AI to an exploration program, this is usually the highest-leverage place to look first, both because the data already exists and because the vendors serving this space (Corescan, TESCAN, Zeiss, CSIRO) have already done the hard work of making their AI classifiers production-grade rather than research prototypes.

Flotation

Mon, 21 Sep 2026 00:00:00 +0800

Flotation is one of the most mature applications of industrial AI in mineral processing: closed-loop reinforcement-learning controllers like Imubit already write setpoints directly to plant control systems rather than just recommending them to an operator. This category covers that closed-loop control approach, the froth-camera vision models that feed it, and soft-sensor models that predict flotation performance without a physical sensor in the loop.

It also covers the plant-integration and ore-blending work - stockpile and plant-feed strategy, mill surveys, and the operational-variability challenges of connecting a model trained on historical data to a live DCS.

Geological Modelling & Resource Estimation

Mon, 21 Sep 2026 00:00:00 +0800

Geological modelling and resource estimation is where AI assists a geologist’s interpretation rather than replacing it: implicit modelling software that infers lithology, structure and alteration from sparse drill data, and statistical models that predict geotechnical and recovery variables directly into a block model.

This category also covers predicting mineralogy and geometallurgical domains and the underlying statistical domaining work that groups a deposit into zones with distinct behaviour before a mine plan is built on top of it.

Geometallurgy Characterization

Mon, 21 Sep 2026 00:00:00 +0800

Geometallurgy characterization turns hyperspectral and mineralogical data into the proxy models a block model actually runs on: clay content, alteration proxies, and recovery and hardness predictions - so the metallurgical response of a deposit can be estimated ahead of mining rather than discovered at the plant.

This category spans the full pipeline: the data-acquisition layer (hyperspectral core scanning at roughly 800,000 spectral samples per meter), the interpretation layer (neural-net tools like CSIRO’s MyLogger), and the dynamic block models and proxy models that consume their output downstream.

Grade Control & Geotechnical Control

Mon, 21 Sep 2026 00:00:00 +0800

Grade control and geotechnical control are mostly a pattern-recognition problem applied to data an operation already collects: advanced sensing (XRF, XRD, LIBS, hyperspectral) calibrated against known grade, automated ore sampling, and bench slope stability monitoring.

The other half of this category is automated reporting: geomet reports, geotechnical reports and monitoring reports that used to take a specialist days to compile now largely assembled from sensor and model output. In practice the same anomaly-detection and classification models feed both the sensing side and the reporting side, which is why they sit in one category rather than two.

Marketing, Costs & Reconciliation

Mon, 21 Sep 2026 00:00:00 +0800

Marketing, costs and reconciliation covers the commercial side of a mining operation: AI-assisted production forecasting for the marketing and sales function that has to sell what the mine actually produces, reconciled against what the geological and mine-planning models predicted.

This is the smallest category on this site today, and the one most likely to grow as operations connect their geological and production-forecasting models directly to commercial planning.

Mine Planning & Drilling/Blasting Design

Mon, 21 Sep 2026 00:00:00 +0800

Mine planning and blast design increasingly run on AI-assisted optimization rather than manual iteration: platforms that generate and evaluate blast designs and blasting sequences, and evolutionary search over long-term production schedules.

This category covers both the design-generation side and the evaluation side: blasting evaluation after the fact, and the mining block models and production-prediction reports that a plan’s assumptions ultimately get checked against.

Ore Sorting, Dispatch & Stockpiling

Mon, 21 Sep 2026 00:00:00 +0800

Ore sorting and dispatch are where sensor fusion earns its keep: belt-mounted grade sensors like BeltSense, SWIR and XRT-based ore-sorting systems, and the dispatch and control-room software that routes trucks and stockpiles based on real-time grade data rather than a fixed plan.

This category covers the sensing hardware, the sorting technologies built on top of it, and the dispatch logic that turns sorted, graded material into a coherent plant-feed strategy rather than a pile that gets reprocessed later.

Thickening & Tailings

Mon, 21 Sep 2026 00:00:00 +0800

Tailings and thickening carries the highest safety stakes in this list: AI-driven dam monitoring that fuses ground radar, piezometers and satellite InSAR to catch failure precursors before they become failures, and the operational side of managing that monitoring data across a facility’s life.

Alongside the safety-critical monitoring, this category covers thickener control itself and the water management plans and environmental impact assessments that a tailings facility’s approval depends on.

Topography

Mon, 21 Sep 2026 00:00:00 +0800

Topography covers the drone and LiDAR survey work that produces the 3D terrain models everything else - blast design, haul-road planning, geological modelling - gets built on top of.

The other half of this small but foundational category is the geological model that terrain and subsurface data are ultimately fused into, which is what the rest of a mine’s planning and design decisions actually reference.