Key Takeaways
- Geometallurgical reports turn geomet sample results into predictive performance reports, and there is a real dedicated platform for them: Cancha.
- Cancha integrates sample selection, prediction modelling and automated reporting into one system, rather than leaving geologists to stitch spreadsheets together.
- This space is still “Emerging”. Cancha is the clearest dedicated product, but the category isn’t yet as crowded as mainstream mine-planning software.
- The AI value is in the prediction modelling step: forecasting recovery, hardness and throughput from geomet sample data rather than manually cross-referencing lookup tables.
TL;DR
To automate geometallurgical reports with AI, feed your geomet test results into a dedicated platform such as Cancha. It selects representative samples, builds prediction models onto your block model and generates the report. Validate its predictions against your own mill data before relying on it.
What Are Geometallurgical Reports?
Geometallurgy links the geology of an ore body to how that ore behaves in the plant. Samples go through metallurgical tests, such as comminution indices, flotation or leach recovery tests, and mineralogy. The results are then spread across the block model, so every block carries a predicted hardness, throughput and recovery.
Geometallurgical reports package those predictions for the people who use them. Mine planners use them to schedule ore. Metallurgists use them to set plant expectations and budgets. As a result, the reports need updating whenever new samples or tests arrive.
How Do I Automate GeoMet Reporting With AI?
Traditional GeoMet reporting is a manual bottleneck. A geologist pulls sample results, cross-references them against lookup tables or Excel regression fits, and writes up a report for mine planning. That process doesn’t scale as sample volumes grow, and it is slow to update. Cancha replaces the pipeline in three steps.
Selecting representative samples
You feed Cancha your geometallurgical sample results: comminution indices, recovery tests and mineralogy. First, it handles sample selection, deciding which samples are representative enough to model from. This matters because metallurgical tests are expensive, so each sample must earn its place.
Predicting performance for geometallurgical reports
Next, it builds prediction models that map sample-scale results onto your block model. Because it is built for this workflow, rather than being a general BI tool bolted onto mining data, the prediction step benefits from domain-specific model choices. For example, it uses suitable regression or classification techniques for recovery and hardness variables instead of a generic linear fit. For the modelling itself, see recovery and hardness prediction with machine learning.
Generating the report output
Finally, it generates the reporting output your mining and metallurgy teams actually consume. When new test results arrive, the models and reports update together. Therefore, planners work from current predictions rather than last quarter’s spreadsheet.
How Mature Is AI for Geometallurgical Reports?
Worth being honest here: this is still an emerging category. Cancha is the clearest dedicated tool we found, but you won’t find the dozen competing SaaS platforms you’d see in mine scheduling software.
If you evaluate it, check its predictions against your own historical data through geometallurgical reconciliation before relying on it alone. Reports are only as good as the block model behind them, so pairing them with dynamic block models keeps predictions current.
FAQs
What goes into geometallurgical reports?
They usually contain predicted hardness, throughput and metallurgical recovery for ore blocks or domains. They also summarise the test data behind those predictions and their uncertainty. Mine planners and metallurgists use them to schedule ore and forecast plant performance.
How does AI help with geometallurgical reports?
AI helps mainly in the prediction step. Machine-learning models map expensive, sparse test results onto the whole block model more flexibly than lookup tables. Automation then regenerates the reports whenever new data arrives.
Is Cancha the only option?
It is the clearest dedicated platform we found, but not the only route. Some teams build their own models in Python or R and report through BI tools. That takes more in-house skill, but gives full control over the modelling.
How often should geometallurgical reports be updated?
Ideally, whenever significant new test data or mill performance data arrives. Automated platforms make frequent updates practical. Many sites still update quarterly, but that leaves planners working from stale assumptions.
Try It With Geocluster
Evaluating whether a platform like Cancha’s prediction models actually fits your deposit, versus building something custom, is a research question worth doing rigorously. The Geocluster Research Harness helps you run that kind of comparative AI-tooling research systematically instead of ad hoc. Read how the Geocluster Research Harness works to see where it fits.