Grade Control & Geotechnical Control on Eigenform AI Geo Tooltips

Advanced sensing

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

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 raw spectra into calibrated grade/mineralogy estimates in real time.
Hyperspectral scanning platforms like Corescan (built on CSIRO’s HyLogging technology) are the most AI-forward of the three, using machine-learned spectral libraries to auto-classify alteration and clay mineralogy.
If you’re evaluating “advanced sensing” as a category, evaluate the calibration/chemometric software behind each instrument, not just the hardware spec sheet.

TL;DR

“Advanced sensing” isn’t one tool — it’s XRF, XRD, LIBS and hyperspectral scanners feeding AI-assisted calibration software that converts raw spectra into grade and mineralogy estimates on the spot.

Automating Ore Sampling with Sensors and AI

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

Key Takeaways

Shovel-face, automated, and blasthole/RC sampling are all being upgraded with sensor packages that reduce manual handling and speed up grade turnaround.
Motion Metrics ShovelMetrics and MineSense’s shovel sensor line use AI/computer vision to estimate fragmentation and grade at the dig face in real time.
Scott Automation’s Rocklabs line (AMS Prep, RoboPrep Elite) automates crushing, splitting, and pulverizing so cross-belt or lab autosamplers can feed inline XRF without manual prep.
IMDEX BLASTDOG specifically instruments blasthole/RC sampling, capturing sensor-while-drilling data as the hole is drilled.
Blasthole/RC sampling AI is the least mature of the three (Emerging) — most of the intelligence currently sits in the downstream XRF/assay step rather than the sampling mechanism itself.

TL;DR

Replace manual scoop-and-bag sampling with sensor-equipped shovels (grade/fragmentation AI), robotic sample-prep lines feeding inline XRF, and instrumented blasthole rigs like BLASTDOG — cutting the lag between digging ore and knowing what’s in it.

Bench Slope Design

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

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 (dip, dip-direction, strength) feed faster, more automated slope-design inputs.
Don’t expect an “AI slope designer” product — expect AI-derived inputs plugged into the same trusted geotechnical engines you already use.

TL;DR

AI-assisted bench slope design means using machine-learning models to generate structural and rock-mass inputs (like dip/dip-direction from drillhole or image logs) faster, then running the actual factor-of-safety analysis in established tools like RocScience Slide2/RS2/RS3 or GeoStudio, same as always.

Blasthole Logging

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

Key Takeaways

Hexagon Drill Assist puts AI directly into the drilling algorithm, automatically managing drill parameters from rig-sensor data as it drills — no manual parameter entry.
Operators report training time dropping from years to about 15 minutes with Drill Assist, alongside real gains in fragmentation, ore dilution reduction, and energy use per metre.
Fully automated computer-vision lithology/cuttings logging is still research-stage — sensor-while-drilling systems from IMDEX and Epiroc are the more mature adjacent capability.

TL;DR

AI blasthole logging today mostly means AI-driven drilling itself — Hexagon Drill Assist manages drill parameters automatically from live rig-sensor data, capturing consistent, high-quality blasthole logs as a byproduct, while automated cuttings/lithology vision-based logging remains a research direction rather than a deployed product.

GeoMet Reports

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

Key Takeaways

GeoMet reporting — turning geometallurgical sample results into predictive performance reports — has a real dedicated platform: Cancha.
Cancha integrates sample selection, prediction modelling, and automated reporting into one system, rather than leaving geologists to stitch spreadsheets together.
This space is still “Emerging” — Cancha is the clearest dedicated product, but the category isn’t yet as saturated with competitors as mainstream mine-planning software.
The AI value is in the prediction modelling step: forecasting recovery, hardness, and throughput from geomet sample data rather than manually cross-referencing lookup tables.

TL;DR

Automate GeoMet reporting with Cancha, a dedicated geometallurgy platform that runs sample selection, predictive modelling, and reporting in one integrated workflow instead of spreadsheets.

Geometallurgical Reconciliation

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

Key Takeaways

Geomet reconciliation — comparing predicted vs. actual mill performance to recalibrate your models — has a documented real-world AI implementation at the Tropicana Gold Mine.
The Tropicana approach uses near-real-time recalibration of Work Index and geomet block models based on the gap between predicted and actual mill throughput/recovery.
Cancha productizes a similar reconciliation workflow for teams that don’t want to build custom ML pipelines in-house.
This is one of the more mature AI applications in the geomet space — it’s “Yes” not “Emerging” because there’s a published, working case study, not just a vendor pitch.

TL;DR

Recalibrate your geomet block model automatically by feeding actual mill performance back into your prediction model — the Tropicana Gold Mine’s published approach does this in near-real-time, and Cancha offers a productized version of the same idea.

Geotechnical Monitoring

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

Key Takeaways

AI-augmented slope radar and satellite InSAR monitoring is now the standard, not the exception, for real-time pit-wall movement detection.
IDS GeoRadar’s Ai.DA adds a machine-learning layer on top of raw radar data to separate real instability trends from noise.
SkyGeo delivers decision-grade satellite InSAR specifically for mining slope and tailings movement, complementing ground-based radar.

TL;DR

AI geotechnical monitoring means running slope radar (IDS GeoRadar) and satellite InSAR (SkyGeo) feeds through machine-learning trend-detection layers so early wall-failure warnings surface automatically instead of requiring a geotechnical engineer to eyeball movement plots continuously.

Geotechnical Reports

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

Key Takeaways

There’s no dedicated “geotechnical reporting” AI product — most sites build BI dashboards on top of their existing modelling platform’s data.
Power BI and Tableau on top of Datamine or Deswik data is the common, pragmatic path.
Cancha is worth a look if you want geotechnical reporting combined with geometallurgical modelling in one platform rather than a bolt-on dashboard.

TL;DR

AI-assisted geotechnical reporting mostly means connecting a BI tool (Power BI/Tableau) directly to your live geotechnical block model and monitoring feeds so reports regenerate automatically instead of being manually compiled — with platforms like Cancha offering a more integrated option if geomet and geotech reporting need to live together.

Monitoring Reports

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

Key Takeaways

Slope-monitoring vendors bundle automated report and alert generation directly into their monitoring platforms — you don’t build this separately.
IDS GeoRadar and SkyGeo both ship automated alerting/reporting as part of the radar and InSAR service, not as an add-on.
This is one of the more mature, deployed AI capabilities on this list — not an emerging or DIY workflow.

TL;DR

AI-generated monitoring reports come built into your slope radar and InSAR monitoring platform — IDS GeoRadar and SkyGeo both auto-generate alerts and periodic reports directly from live sensor data, so there’s no separate reporting tool to stand up.

Structural Mapping

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

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 are increasingly built into these platforms, reducing manual digitizing of joint sets from point clouds.
This is a commercial, deployed capability, with the AI/automation layer still emerging on top of an already-mature photogrammetry foundation.

TL;DR

AI-assisted structural mapping means capturing pit-wall point clouds via laser scan or drone photogrammetry in Maptek PointStudio (or CSIRO’s Sirovision), then using increasingly automated discontinuity-detection algorithms to extract joint orientation, spacing, and trace length instead of hand-digitizing every structure.