Traditional ore body modelling relies on interpolating between drill holes – essentially connecting the dots to estimate what lies between known data points. Machine learning is changing this approach fundamentally, and the implications for resource estimation and mine planning are significant.

The Limitations of Traditional Methods

Geostatistical methods like kriging have served the industry well for decades. They provide mathematically rigorous estimates of grade distribution and uncertainty. But they have inherent limitations.

Linear assumptions: Traditional methods assume linear relationships between distance and grade similarity. Reality is often more complex, with geological structures creating non-linear patterns.

Limited variable integration: Conventional approaches struggle to incorporate multiple data types effectively. Geological information, geophysical surveys, and geochemical data often live in separate models.

Human-dependent boundaries: Domain boundaries – the outlines of mineralised zones – are typically drawn manually by geologists. Different geologists can draw significantly different boundaries from the same data.

Machine learning offers new approaches to these challenges.

How Machine Learning Changes the Game

Modern ML approaches to ore body modelling leverage the ability to identify complex patterns in high-dimensional data.

Neural network grade estimation: Deep learning models can learn complex, non-linear relationships between drill hole locations and grades. They can capture geological patterns that elude traditional methods.

Automated domain classification: Machine learning can identify geological domains directly from data, reducing subjective interpretation. Clustering algorithms group samples with similar characteristics automatically.

Multi-data integration: ML models naturally incorporate diverse data types. Geophysical surveys, lithological logs, and assay data can all inform the same model.

Uncertainty quantification: Modern approaches provide probabilistic outputs, giving geologists and mine planners better information about estimation confidence.

Real-World Applications

Several mining companies have now deployed ML-enhanced ore body modelling with encouraging results.

Grade control improvement: One gold operation reported a 15% reduction in grade reconciliation variance after implementing ML-based grade estimation. Better predictions meant better mining decisions.

Drill hole optimisation: ML models can identify where additional drilling will most effectively reduce uncertainty. This optimises exploration spend by targeting information gaps.

Domain boundary refinement: Automated domain classification has identified mineralised zones that traditional interpretation missed. The value of these “found” resources can be substantial.

Processing optimisation: Better ore body models improve downstream processing. When mills receive material matching predictions, recoveries improve and costs decrease.

The Data Challenge

Machine learning requires data – lots of it. This presents challenges for mining applications.

Historical data quality: Many mines have decades of drill hole data, but quality varies. Cleaning and standardising historical data is often a major project.

Data density requirements: ML models typically need more data points than traditional methods to train effectively. Sparse drilling campaigns may not support advanced modelling.

Feature engineering: Deciding which data attributes to include in models requires geological expertise. The best results come from combining domain knowledge with ML capability.

These AI specialists working in mining applications must address these data challenges. Off-the-shelf solutions rarely account for the specific data quality issues and geological complexity of individual deposits.

Implementation Considerations

Mining companies considering ML-enhanced ore body modelling should consider several factors:

Start with clean use cases: Initial implementations work best with well-constrained problems. Grade control in operating mines provides immediate feedback on model accuracy.

Maintain geological oversight: ML models should enhance geological interpretation, not replace it. Geologists remain essential for validating model outputs and identifying geological nonsense.

Invest in data infrastructure: ML initiatives often expose data management weaknesses. Addressing these improves broader operational capability.

Plan for iteration: Initial models rarely deliver optimal results. Plan for multiple cycles of training, testing, and refinement.

The Integration Challenge

The most advanced ore body modelling combines traditional methods with machine learning in ensemble approaches. This requires careful integration.

Geological block models interface with mine planning systems, processing optimisation, and financial reporting. Changes to modelling methodology ripple through these connected systems.

Working with firms offering AI implementation help who understand mining workflows ensures ML models integrate effectively with existing operations.

Validation and Verification

Mining companies are rightly cautious about adopting new modelling methods. Resource estimates have significant financial and regulatory implications.

Reconciliation studies: Comparing ML predictions against actual mining results provides objective validation. This requires operating mines with good measurement systems.

Cross-validation: Withholding portions of data during model training, then testing predictions against withheld data, quantifies model accuracy.

Comparison with traditional methods: Running ML and traditional models in parallel allows direct comparison of performance.

External review: Independent geological review of ML-derived models provides additional assurance for public reporting.

Looking Forward

Machine learning in ore body modelling is still maturing. Current applications focus on specific problems where ML demonstrably outperforms traditional methods.

Future developments will likely include:

• Real-time model updating as new drilling and mining data becomes available
• Integration with autonomous drilling to optimise exploration in real-time
• Extended prediction capabilities for metallurgical parameters beyond grade
• Improved uncertainty visualisation to support risk-informed decisions

The goal isn’t to replace geologists with algorithms. It’s to give geologists better tools for understanding complex ore bodies and making better decisions with limited data.

Machine learning won’t eliminate geological uncertainty – but it can help mining companies manage it more effectively.