Key Takeaways

  • First-pass (“quick”) geological logging — the initial lithology/alteration read on fresh core — is one of the more mature AI use cases in exploration.
  • Datarock Core and Seequent Central’s logging module both support AI-accelerated first-pass logging directly from core photography.
  • Datarock has processed over 30 million metres of core since 2020, turning what would be months of manual logging into hours.
  • This is a “Yes” — already deployed, production-grade AI, not a research prototype.

TL;DR

Feed high-resolution core photography into a tool like Datarock Core to get an automated first-pass lithology/structure log in hours instead of the days or weeks manual logging takes, then have your geologists review and refine rather than log from scratch.

How Do I Automate Geological Logging With AI?

Quick geological logging exists because geologists need an immediate read on what’s coming out of the ground — long before detailed petrography or assay results are back — to make real-time decisions about hole depth, target changes, and drilling priorities. Historically that’s meant a geologist walking the core shed with a hand lens. Today, it increasingly starts with a camera.

Datarock Core automates this by extracting geotechnical and geological information directly from standard RGB core imagery: rock type boundaries, fracture frequency, discing, and structural features, delivered as a structured, auditable log within hours of the core arriving at the shed. It plugs into existing core-shed workflows rather than replacing them — geologists still validate and interpret, but they’re starting from an AI-generated first draft instead of a blank log sheet. Seequent Central’s logging module offers a complementary angle: it keeps that quick-logged data centralized, version-controlled, and immediately visible to the rest of the geoscience team in 3D, so a “quick log” done at the rig doesn’t sit disconnected from the block model until someone manually re-enters it later.

The evidence base here is real, not speculative — Datarock’s fracture-detection AI has been validated against expert manual logging in published case studies (including at the Carrapateena copper-gold mine), and the technology is in production use at major mining companies, not just pilot programs.

Try It With Geocluster

Getting from “AI logged the core” to “here’s what it means for the deposit model” still takes synthesizing logging output against prior geological knowledge, analog deposits, and exploration targets. That’s the kind of research work Geocluster is designed to accelerate — check it out if you’re building this into your exploration pipeline.