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.

How Do I Analyze SWIR Mineral Data With AI?

SWIR spectroscopy measures how a rock or mineral surface reflects light in the short-wave infrared band, and different clay and alteration minerals produce distinctive absorption features in that range that are invisible to the naked eye or standard RGB photography. This makes it particularly valuable for identifying alteration mineralogy — a first-order input for both exploration targeting and geometallurgical clay prediction (clays drive flotation and comminution behavior significantly).

For continuous, automated coverage, Corescan’s Hyperspectral Core Imager scans drill core at roughly 800,000 spectral samples per meter across the 450-2,500nm range (VNIR through SWIR), co-registering the spectral data with core photography and 3D laser profiling in a single pass. This is a service/instrument model — Corescan operates it both as turn-key regional bureaus and on-site deployments — rather than something you buy and run entirely in-house. For spot checks or portable field use, Malvern Panalytical’s ASD TerraSpec is the established handheld alternative, widely used for fast pathfinder mineral identification directly in the field or core shed.

Where this connects to AI: raw spectral curves aren’t directly useful to a geologist or a downstream model — they need to be classified into mineral species and abundance estimates first, which is where automated spectral-matching algorithms (and increasingly ML classifiers trained on labeled spectral libraries) come in. Once you have classified mineralogy per meter of core, that becomes an input to the clay/alteration proxy models used in geometallurgical block modeling, and — increasingly — a real-time input to sensor-fusion ore sorting, where SWIR is paired with XRT or optical sensors on systems like Steinert’s KSS to add mineralogical discrimination on top of density-based sorting.

Try It With Geocluster

Working out whether continuous automated scanning (Corescan) or portable spot-checking (ASD TerraSpec) fits your program’s scale and budget — and how to get the resulting spectral data usefully into your geomet models — is exactly the kind of applied research Geocluster can help you think through.