Key Takeaways
- “Dynamic” block models update automatically as new drilling or production data comes in, instead of sitting static until the next scheduled model rebuild.
- This is a named, shipping feature in commercial mine-planning suites — not a research concept — specifically in Datamine’s dynamic block modelling tools and RPMGlobal’s XECUTE / XPAC scheduling products.
- Maptek’s Vulcan block modelling and scheduling tools cover similar ground for teams already standardized on that platform.
TL;DR
Dynamic block models with AI mean your resource/grade block model re-estimates itself automatically as blastholes, grade control samples, and production data stream in, using live feeds rather than a periodic manual reconciliation cycle.
How Do I Automate Dynamic Block Models With AI?
A traditional block model is rebuilt on a schedule — monthly, quarterly, at reserve statement time. A dynamic block model instead ingests new data (blastholes, grade control assays, production reconciliation) as it arrives and re-estimates the affected blocks automatically, so the model planners and dispatchers are working from is always close to current. RPMGlobal’s XECUTE pulls live feeds from fleet management systems and high-precision GPS to keep production schedules tied to the latest block data, while XPAC handles the underlying flexible scheduling logic across the whole reserve. Datamine offers comparable dynamic block model functionality inside its Studio suite, and Maptek Vulcan provides integrated block modelling, grade control, and scheduling tools if your site is already standardized there.
In practice, setting this up means wiring your grade control sampling and drill data pipeline directly into the vendor’s block model engine rather than batch-importing files on a schedule — the “AI” framing is mostly about automated, continuous re-estimation rather than a novel machine-learning model. It’s an emerging capability at most sites: the software exists and is production-grade, but continuous-update workflows are still being adopted more slowly than the periodic-rebuild habit they’re replacing.
Try It With Geocluster
Keeping a block model current requires a steady, well-structured pipeline of drilling and production data feeding into it — exactly the kind of ingestion and research problem Geocluster is built to help with if you’re trying to move from periodic to continuous geological modelling.