Hyperspectral Imaging 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.

AI Image Analysis for Ore Texture, Grain Size and Mineral Mapping

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

Key Takeaways

Textural analysis, grain size analysis, and mineral mapping run on the same automated-mineralogy and hyperspectral imaging stack: TESCAN TIMA, Zeiss Mineralogic, and Corescan for core-scale hyperspectral work.
These platforms increasingly use CNN-based classifiers rather than fixed spectral lookup tables to resolve grain boundaries and mixed-pixel mineralogy.
Output feeds two downstream uses directly: comminution/liberation modeling (how finely you need to grind to liberate value minerals) and geometallurgical domaining.
This is a mature, commercially deployed capability (ai_relevant: Yes across all three) — not a research prototype.
Malvern Panalytical’s Morphologi line is a relevant alternative specifically for particle-scale grain-size distribution work outside the SEM-based platforms.

TL;DR

Run polished samples or drill core through an automated SEM mineralogy platform (TIMA, Mineralogic) or hyperspectral core scanner (Corescan) to get quantitative texture, grain-size, and mineral-map data with built-in ML-based classification, rather than manual petrographic description.

Alteration/Proxies Model

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

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 themselves.
Corescan’s hyperspectral-derived alteration maps are increasingly the data source feeding those surfaces, rather than logged intensity scores alone.
Still “Emerging” — the hyperspectral-to-alteration-domain pipeline isn’t a single push-button product yet, it’s an integration you build.

TL;DR

Build your 3D alteration surfaces in Leapfrog Geo’s implicit modelling engine, but feed them from Corescan hyperspectral alteration maps instead of relying solely on a geologist’s logged alteration intensity — you’ll get a denser, more objective input signal.

Clay/Geomet Model

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

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.
CSIRO’s new MyLogger tool applies trained neural networks to interpret HyLogger spectra directly into a geological log — a genuinely mature, purpose-built AI step, not a research prototype.
This is a real “Yes” for AI-relevant today, not an emerging/speculative category.

TL;DR

Scan your core with Corescan or HyLogger, run the spectra through a neural-net interpretation layer like CSIRO’s MyLogger to get first-pass clay/alteration mineralogy automatically, then feed that directly into your geomet block model instead of waiting on manual spectral interpretation.

Core Photography

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

Key Takeaways

High-resolution automated core photography is the raw data source that most downstream AI core-analysis tools depend on.
Corescan (now Epiroc), Datarock Core, and Minalyze all offer automated core photography systems designed to feed AI extraction pipelines.
This is a “Yes” — production-grade, already deployed across major mining companies.
Get the photography pipeline right first: every AI fracture/lithology/mineralogy tool downstream is only as good as the image quality and consistency it’s trained/run on.

TL;DR

Set up automated, standardized high-resolution core photography (via Corescan, Datarock, or Minalyze hardware) as the foundation — it’s the input every downstream AI logging, fracture-detection, and mineralogy tool depends on.

Core Scanning

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

Key Takeaways

Hyperspectral core scanning captures mineralogy and alteration information invisible to the naked eye, and it’s one of the most mature AI-adjacent technologies in exploration geology.
Leading systems: Corescan HCI-3, CSIRO/Epiroc HyLogger 4, and Minalyze MCore.
HyLogger 4 is the world’s first continuous visible-to-thermal-infrared core scanner (0.4–15 microns), adding mid-infrared for minerals invisible to earlier generations.
This is a “Yes” — deployed at national core libraries (Australia) and commercial operations, not experimental.

TL;DR

Run drill core through a hyperspectral scanner (HyLogger 4, Corescan HCI-3, or Minalyze MCore) to automatically generate mineralogy and alteration maps along the entire core length — data that would otherwise require selective, time-consuming point sampling with a spectrometer or petrographic microscope.

Density Test

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

Key Takeaways

Physical density testing (water immersion, wax coating, gas pycnometry) has no dedicated software of its own — it’s a bench measurement, not a computational one.
The AI opportunity is predicting density from cheaper, faster proxy measurements instead of running the physical test on every sample.
A January 2026 peer-reviewed study, A Multi-Proxy Framework for Predicting Ore Grindability, shows portable XRF, Leeb hardness, and hyperspectral imaging can stand in for slower physical rock-property tests with real predictive power.

TL;DR

You don’t AI-ify the density test itself — you train a regression model on hyperspectral, XRF, and hardness proxy data to predict density (and related grindability parameters) without running the physical test on every single sample.

Hyperspectral (Point or Scan)

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

Key Takeaways

Two flavors exist: handheld point spectrometers (fast, spot checks) and full-core imaging scanners (slow, exhaustive, image-based).
The core technology (VIS-NIR-SWIR reflectance spectroscopy) is mature and commercial; the “AI” part is mostly in the automated mineral-classification layer sitting on top of the raw spectra.
Malvern Panalytical’s ASD TerraSpec line handles the point-measurement use case; Corescan’s Hyperspectral Core Imager handles full-core scanning.
This is best treated as a data-acquisition layer, not a standalone AI product — the value shows up downstream in alteration and clay models.

TL;DR

Use a point spectrometer for quick field/spot mineral ID and a full-core hyperspectral scanner for systematic alteration mapping — then feed the spectra into a classification model to actually get AI value out of it.

Hyperspectral imaging / Spectroscopy Device

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

Key Takeaways

Portable VIS-SWIR spectrometers like the ASD TerraSpec Halo (Malvern Panalytical) identify minerals in the field or on core in seconds via spectral-library matching.
Note: as of this writing, the TerraSpec Halo itself is listed as no longer available to purchase from Malvern Panalytical — check current availability and successor products (TerraSpec 4) before specifying it for a new program.
SciAps also offers a field spectrometer line worth comparing.
The “AI” here is the spectral-matching engine comparing your field reading against a library of thousands of reference mineral spectra — genuinely ML-adjacent, but framed by vendors as spectral matching rather than a general AI model.

TL;DR

Point a portable VIS-NIR-SWIR spectrometer (ASD TerraSpec range, or a comparable current model) at a rock face, drill core, or hand sample, and its onboard spectral-library matching identifies the alteration minerals present in seconds — the same core logic that hyperspectral core-scanning platforms use, just handheld.

Hyperspectral Input Models (High Resolution Clay Models)

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

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 this an AI workflow rather than just a scanning workflow.
These systems generate roughly 800,000 spectral samples per meter of core — the resolution is what enables high-confidence clay models, not just the sensor’s presence.
This is a solid “Yes” — mature, deployed technology, not a speculative research direction.

TL;DR

High-resolution clay models come from running core through a hyperspectral scanner (Corescan HCI or CSIRO HyLogger) at sub-millimeter resolution, then using a trained interpretation model (like MyLogger’s neural network) to convert the spectra into quantitative clay mineralogy at every sampled point.

Predicting Comminution Parameters (BWI, DWI, Ai) with Machine Learning

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

Key Takeaways

BWI (Bond Work Index), DWI (Drop Weight Index), and the Abrasion Index all still start from physical rock-breakage tests, run through JKTech’s JK Drop Weight Test / JKSimMet — the industry-standard equipment and simulation software.
2025 research validates cheaper proxies — hyperspectral imaging (HSI) and Leeb rebound hardness — predicted by ML models as low-cost stand-ins for full BWI testing.
A 2025 deep neural network approach (published in Minerals Engineering) predicts DWI/BWI from Geopyörä breakage test data trained across ~700 global ore samples, cutting lab turnaround time.
The Abrasion Index has no dedicated AI-native product yet — it rides on the same ML grindability-proxy research as BWI/DWI, making it the least mature of the three (Emerging).
None of this replaces physical testwork entirely — it reduces how many full tests you need across a deposit by predicting the rest from cheaper measurements.

TL;DR

Instead of running full Bond/drop-weight tests on every sample, run a cheap proxy measurement (hyperspectral scan or Leeb rebound hardness) on everything and a full physical test on a calibration subset, then train a regression model to predict BWI/DWI/Ai across the rest of the deposit.

SWIR

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

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 ASD TerraSpec are the two dominant named instruments — one built for continuous automated core scanning, the other a portable handheld spectrometer.
SWIR data is increasingly fused into ML-based geomet and ore-sorting models rather than used only for manual mineral identification by a geologist.
This is a data-acquisition technology, not a decision-making AI on its own — the AI value comes from what you build on top of the spectral data (clay models, alteration domaining, sorting logic).

TL;DR

Getting SWIR into your AI workflow means scanning core or ore with a hyperspectral instrument — Corescan’s HCI for automated continuous core logging, or a portable ASD TerraSpec for spot measurements — then feeding the resulting spectral data into downstream clay/alteration prediction models or sorting logic.