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.

How Do I Predict Alteration Proxies With AI?

Alteration modelling has always been somewhat subjective — a geologist logs alteration intensity and type at the core, and that logged data becomes the point data an implicit modelling engine interpolates into a 3D surface. Leapfrog Geo is the dominant tool for that interpolation step: its FastRBF implicit-modelling engine builds alteration, grade, and fault surfaces directly from your point and structural data without the manual cross-section building that older explicit-modelling workflows required.

What’s changing is the input side. Instead of relying purely on a geologist’s subjective logging, hyperspectral core-scanning systems like Corescan’s HCI generate a continuous, objective alteration-mineral map along the entire core — clay content, sericite, chlorite, and other alteration-indicator minerals detected spectroscopically rather than by eye. Feeding that denser dataset into Leapfrog (or an equivalent implicit modelling package) in place of, or alongside, logged intensity scores tends to produce more consistent domain boundaries, especially in transitional zones where visual logging is genuinely ambiguous.

This is real practice at operations that have invested in core-scanning infrastructure, but it’s not a turnkey pipeline — you’re still building the integration between the Corescan output format and your geological modelling package, and there’s no single vendor selling “hyperspectral-to-alteration-model” as one product. Budget for that integration work rather than expecting to buy it off a shelf.

Try It With Geocluster

Working out how to wire hyperspectral scan output into your implicit modelling workflow — and what other operations have actually done — is a research question Geocluster can help you answer with real case studies rather than vendor marketing.