Key Takeaways
- Dynamic block models update automatically as new drilling or production data comes in, instead of sitting static until the next scheduled rebuild.
- This is a named, shipping feature in commercial mine-planning suites, not a research concept. It appears in Datamine’s dynamic block modelling tools and RPMGlobal’s XECUTE and XPAC scheduling products.
- Maptek’s Vulcan block modelling and scheduling tools cover similar ground for teams already standardised on that platform.
TL;DR
Dynamic block models with AI re-estimate your resource and grade model automatically as blastholes, grade-control samples and production data arrive. In practice, you wire those data feeds straight into your vendor’s block model engine instead of batch-importing files. The result is a model that planners and dispatchers can trust as current.
What Are Dynamic Block Models?
A block model divides a deposit into a 3D grid of blocks. Each block carries estimated grade, density, rock type and often geometallurgical attributes such as hardness or recovery. Mine planners schedule from it, and dispatch uses it to send each truckload to the right destination.
Traditionally, the block model is rebuilt on a schedule: monthly, quarterly or at reserve statement time. Between rebuilds, new information piles up unused. Dynamic block models change that. They ingest new data as it arrives and re-estimate only the affected blocks. As a result, the model stays close to what the mine is actually digging.
How Do I Automate Dynamic Block Models With AI?
Setting this up is mostly a data-pipeline project, followed by a scheduling link and a validation loop.
Connect live data to dynamic block models
Start with the inputs. Blasthole assays, grade-control samples and drill logs need to flow straight into the block model engine. That replaces the habit of exporting and importing files by hand. Faster, cleaner inputs from blasthole logging make the biggest difference here.
Tie the model to scheduling and dispatch
Next, connect the updated model to the tools that use it. RPMGlobal’s XECUTE pulls live feeds from fleet management systems and high-precision GPS. It keeps production schedules tied to the latest block data. Meanwhile, XPAC handles flexible scheduling logic across the whole reserve. Datamine offers comparable dynamic block model functionality inside its Studio suite. Maptek Vulcan provides integrated block modelling, grade control and scheduling if your site already runs on it.
Validate dynamic block models against production
Finally, check that the updates actually improve the model. Compare predicted tonnes and grade with what the mill receives. Geometallurgical reconciliation closes that loop and shows whether continuous updating beats the old monthly rebuild.
What “AI” Means Here
Be honest about the label. The “AI” framing is mostly about automated, continuous re-estimation rather than a novel machine-learning model. The estimation itself often still uses kriging or other standard geostatistics. However, machine learning can enter through the attributes each block carries, for example predicted recovery or hardness.
It is also an emerging capability at most sites. The software exists and is production-grade. Even so, continuous-update workflows are replacing the periodic rebuild of a static mining block model only slowly. Governance is a common reason: resource geologists need to sign off on what changed and why.
FAQs
What is the difference between a static and a dynamic block model?
A static block model is rebuilt on a fixed schedule, such as monthly or quarterly. A dynamic block model updates the affected blocks whenever new drilling, grade-control or production data arrives. Therefore, the dynamic version stays closer to current conditions between rebuilds.
Which software supports dynamic block models?
Datamine offers dynamic block modelling tools in its Studio suite. RPMGlobal’s XECUTE and XPAC link live production data to scheduling. Maptek Vulcan provides integrated block modelling, grade control and scheduling.
Do dynamic block models use machine learning?
Mostly, they use automation and standard geostatistics rather than a new machine-learning method. Machine learning can still add value by predicting block attributes such as recovery or hardness. The main change is how often the model updates.
What do you need before setting up dynamic block models?
You need a reliable, well-structured feed of drilling, grade-control and production data. You also need clear rules for who approves model changes. Without those, continuous updates can create confusion rather than clarity.
Try It With Geocluster
Keeping a block model current requires a steady, well-structured pipeline of drilling and production data. Building that pipeline is exactly the kind of ingestion and research problem Geocluster is built to help with. It is worth a look if you want to move from periodic to continuous geological modelling.