Key Takeaways

  • “Informed” logging means the geologist (or AI system) logs new core with the benefit of prior geological/geophysical model context, not in isolation.
  • Datarock Core and Seequent Central’s logging module both support this by surfacing existing model context alongside new imagery.
  • This is a “Yes” — the underlying AI core-logging technology is production-proven; what’s new is using it context-aware rather than hole-by-hole in isolation.
  • The practical benefit: consistency across a drill program, since every hole gets logged against the same evolving model rather than each geologist’s independent read.

TL;DR

Pair an AI core-logging tool like Datarock Core with a centralized model platform like Seequent Central so that each new hole is logged with the benefit of everything already known about the deposit, rather than being interpreted in isolation.

How Do I Improve Informed Logging With AI?

The problem “informed logging” solves is a subtle but important one: a geologist logging hole #40 of a drill program should ideally have the context of holes #1–39 and the evolving geological model in front of them — but in practice, logging often happens hole-by-hole, shift-by-shift, with limited time to cross-reference prior results. That’s how inconsistency creeps into a logging dataset over a long campaign.

Datarock Core’s AI logging extracts geological and geotechnical features directly from core photography — rock type, structure, fracture frequency — as a first-pass, standardized read that doesn’t degrade in consistency the way manual logging can over a long, multi-geologist program. Layer that on top of Seequent Central, which keeps the evolving 3D geological model, prior logging, and drillhole data centralized and visible to the whole team, and you get the “informed” part: new core gets logged with the model in view, not blind.

In practice this looks like: core photography flows into Datarock for automated first-pass logging, that output lands in Central alongside the existing geological model, and geologists review/adjust the AI log with the broader model context on screen rather than working from a printed log sheet at the core shed. The result is a logging dataset that’s both faster to produce and more internally consistent across a long drill program — which matters a lot when different geologists are logging different holes weeks or months apart.

Try It With Geocluster

Keeping the “context” behind informed logging up to date — the latest interpretation, comparable deposits, relevant literature — is a research-synthesis task in its own right. Geocluster is built to help geoscience teams pull that context together quickly.