Ore Misclassification Starts at the Drill - Here’s How to Stop It

From Drill to Stockpile: Where Ore Misclassification Happens and How to Stop It

At NTWIST, we’ve learned that ore misclassification is rarely the result of a single bad decision. It’s the compounding effect of subtle inaccuracies - introduced during drilling, modeling, blasting, and hauling - that accumulate and distort everything from stockpile routing to metallurgical reconciliation.

If your stockpiles aren’t performing to plan, the problem may not be with your reclaim strategy. It may have started upstream - long before the shovel hit the ground. In this article, we walk through the chain of ore misclassification and explain how to intercept it before it becomes permanent loss.

The Hidden Gaps in Ore Classification

Orebody modeling relies on drill data - often sparse, interpolated, and statistically smoothed. These models form the foundation of grade control plans. But between the model and the mill, several things go wrong:

• Sampling uncertainty: Drill spacing and biased sampling introduce error margins that are hard to quantify.
• Blast-induced displacement: Without correction vectors or modeling, blast movement can shift material by meters.
• Geological oversimplification: Dig boundaries are generalized into classifications that don’t reflect true in-situ variability.

According to a 2021 MDPI study, errors from sampling and blast movement contribute to ore losses of up to 19% and ore misclassification rates as high as 20% in open-pit operations (MDPI, 2021).

How Misclassification Becomes Material Loss

Once material is misclassified - say, a 1.1 g/t Au zone is logged as 0.8 - it’s often rerouted, rehandled, or diluted. That single discrepancy now distorts stockpile composition, processing expectations, and ultimately, recovery yield.

Even worse, these errors aren’t usually caught in the moment. They show up weeks or months later as metallurgical variance, feed inconsistencies, or reconciliation gaps. The opportunity to correct it is already gone.

One study of South African gold mines showed that misclassification and dilution were responsible for up to 30% of ore loss when upstream plans didn’t reflect in-mill conditions (Scielo, 2016).

How to Intercept Misclassification Before It Hits the Mill

Fixing this requires more than a better model. It requires a system that sees variance early - and adapts the plan before trucks move.

Here’s how we’ve helped mines reduce classification error using NTWIST’s integrated stack:

• Geological model validation: Reconcile predicted grades with production data to detect model drift over time.
• Blast offset modeling: Use historical blast movement data and in-situ vectors to improve dig boundary accuracy.
• Shovel-scale classification: Deploy dig zone reclassification tools based on real-time sensor or operator data.
• Dynamic stockpile routing: Adjust truck assignments based on updated classifications—not static plan assumptions.

This creates a living model that evolves as ore is drilled, blasted, and extracted - protecting material from misrouting before it ever reaches the stockpile.

3 Strategic Takeaways

1. Modeling is only as good as your correction layers. If you don’t adjust for blast and sampling variance, you’re misclassifying material every day.
2. Feedback loops fix models faster. Systems that learn from mill performance can improve classification accuracy upstream.
3. Misclassification is a system failure, not an operator error. The right tools can catch what even skilled teams can’t see in real time.

Conclusion: Classification Drives Everything

If you're seeing recovery losses, mill instability, or stockpile unpredictability, start by tracing your ore classification process. Most mines don’t have a routing problem - they have a resolution problem.

At NTWIST, we enable high-resolution classification and routing by connecting geological models, dispatch systems, and metallurgical feedback into one adaptive loop. It’s time to stop guessing what went wrong - and start knowing what to fix.

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MDPI. (2021). Modelling Large Heaped Fill Stockpiles Using FMS Data. Retrieved from https://www.mdpi.com/2673-6489/2/1/5

Scielo. (2016). Monitoring Ore Loss and Dilution for Mine-to-Mill Integration. Retrieved from https://www.scielo.org.za/scielo.php?pid=S2225-62532016000200009&script=sci_arttext