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.

How Do I Optimize Point and Scan Hyperspectral With AI?

“Hyperspectral” in exploration and geometallurgy covers two distinct workflows that get lumped together on planning diagrams. The first is point measurement: a geologist in the field or at the core shed points a handheld spectrometer — the ASD TerraSpec 4 or its now-discontinued predecessor, the TerraSpec Halo — at a rock face or core sample and gets a spectral match against a library of thousands of reference minerals in seconds. This is fast, cheap per-sample, but sparse: you only see what you point at.

The second workflow is full-core imaging, where a system like Corescan’s Hyperspectral Core Imager (HCI-3/HCI-4, now sold through Epiroc) runs an entire drill core past a VNIR-SWIR sensor plus a visual camera and laser profiler, collecting roughly 800,000 spectral samples per meter. That density is what actually makes downstream AI useful — a sparse handheld dataset isn’t enough training data for a classifier, but a dense core scan is.

Neither system is “AI” out of the box — they’re spectrometers. The AI angle (and where this is genuinely still emerging rather than mature) is in the classification/interpretation layer that turns raw reflectance curves into mineral percentages, alteration intensity, or clay content — the kind of neural-net interpretation CSIRO’s MyLogger applies to HyLogger data, or the regression pipelines that turn Corescan output into clay/geomet block model inputs. If you’re evaluating this stack, budget separately for the scanner hardware and for the interpretation model — they’re sold and built differently.

Try It With Geocluster

Turning raw spectral scans into a defensible geomet model means wiring together instrument output, reference spectral libraries, and a classification model — exactly the kind of multi-source research workflow Geocluster is built to orchestrate. If you’re stitching hyperspectral data into your exploration pipeline, it’s worth a look.