Ore body estimation has always been an exercise in managing uncertainty. You drill a few hundred holes into a deposit, pull out core samples, assay them, and then use geostatistical methods to infer what’s happening in the vast spaces between those holes. It’s educated guessing — sophisticated, mathematically rigorous educated guessing — but guessing nonetheless.

The problem is that even small errors in grade estimation compound into massive financial consequences. Overestimate the grade of an ore block by 10% and your processing plant receives feed that doesn’t match the plan. Underestimate it and you might send economic ore to the waste dump. Either way, money evaporates.

AI-powered geological modeling is starting to chip away at this problem in ways that traditional approaches haven’t been able to.

What Traditional Methods Get Wrong

Conventional ore body estimation relies heavily on kriging and its variants — ordinary kriging, indicator kriging, multiple indicator kriging. These are well-understood geostatistical techniques that have been the industry standard for decades. They work by assigning weights to nearby drill hole data based on distance and spatial correlation, then interpolating grades for unsampled locations.

The issue isn’t that kriging is bad. It’s that it makes assumptions about geological continuity that don’t always hold. Kriging assumes the spatial relationships it calculates from the data are consistent across the deposit. But ore bodies aren’t uniform mathematical surfaces. They’re the product of complex geological processes — folding, faulting, hydrothermal alteration, supergene enrichment — that create abrupt changes in grade, geometry, and mineralogy.

In deposits with strong geological controls on mineralization — think fault-bounded gold systems, or stratabound base metal deposits with sharp lithological contacts — the traditional approach can struggle to capture boundaries accurately. The result is what geologists call “grade smearing,” where kriging smooths out real variations and produces estimates that are less extreme (both higher and lower) than reality.

For ore grade optimization, this matters enormously. If your block model doesn’t capture the sharp boundary between 2.5 g/t gold and 0.3 g/t waste, your mining plan sends waste rock to the mill and ore to the dump. Reconciliation suffers. Costs escalate. Investors ask uncomfortable questions.

How AI Approaches the Problem Differently

Machine learning models for geological estimation take a fundamentally different approach. Instead of relying purely on spatial statistics, they incorporate multiple data types simultaneously — drillhole assays, lithological logging, structural measurements, geophysical surveys, geochemical sampling, and even drill monitoring data (rate of penetration, vibration, torque).

The key advantage is pattern recognition across these diverse datasets. A neural network can learn that a particular combination of magnetic response, drill penetration rate, and alteration style is predictive of higher-grade mineralization, even when the spatial correlation in the assay data alone is ambiguous.

Several architectures are showing promise:

• Deep neural networks trained on multivariable datasets, capturing nonlinear relationships between geological features and grade
• Random forests and gradient boosting models that handle mixed data types (continuous grades, categorical lithology, structural measurements) naturally
• Graph neural networks that model the spatial connectivity of geological domains more flexibly than traditional variograms
• Generative models that produce multiple equally-probable grade scenarios, helping quantify the uncertainty in ways that a single kriged estimate can’t

Companies like Maptek are integrating machine learning capabilities into their geological modeling platforms, and several purpose-built AI tools have appeared from startups focused specifically on resource estimation.

What the Accuracy Gains Look Like

The numbers emerging from operations that have deployed AI-enhanced estimation are genuinely interesting.

A copper-gold operation in South Australia compared AI-generated block models against their traditional kriged models over a twelve-month production period. The AI model reduced grade estimation error by 18% for copper and 22% for gold, measured against reconciled production data. The practical impact was a 6% reduction in ore misclassification — blocks that were classified as ore but reconciled as waste, or vice versa.

At an iron ore operation in the Pilbara, machine learning models incorporating drill monitoring data improved the accuracy of iron grade estimates in areas of complex geological contacts. The traditional model had a reconciliation factor of 0.91 (meaning actual grades were consistently 9% lower than predicted). The AI model brought that to 0.97.

These improvements translate directly to dollars. Better ore/waste discrimination means less waste rock sent to the mill (reducing processing costs per tonne of metal) and less ore sent to the waste dump (recovering revenue that would otherwise be lost). For a mid-tier operation processing 5 million tonnes per year, even a 3% improvement in classification accuracy can be worth $5-15 million annually depending on the commodity.

At team400.ai, there’s useful thinking on how organisations approach AI implementation across technical domains — the challenges in mining aren’t dissimilar to those in other industries adopting ML for complex decision-making.

Where AI Geological Modeling Falls Short

It’s not all good news. There are real limitations.

Data dependency. AI models need training data — lots of it. For a mature operation with thousands of drillholes and years of production reconciliation data, there’s plenty to work with. For a greenfields exploration project with 50 drillholes, there isn’t. Traditional geostatistics actually handles sparse data more gracefully in most cases.

Interpretability. A kriged block model is transparent. You can see the variogram, understand the search parameters, and explain to an auditor exactly why block 4,523 has an estimated grade of 1.8 g/t. A deep neural network’s prediction for the same block is essentially a black box. This matters for JORC Code compliance, where Competent Persons need to demonstrate that estimation methods are appropriate and well-understood.

Geological plausibility. Neural networks can produce estimates that are statistically accurate but geologically nonsensical — predicting high grades in locations that make no geological sense based on structural or lithological controls. Without constraints, the model might fit the data perfectly while violating basic geological principles.

Predictive maintenance crossover. Some operators are tempted to use the same AI platform for both geological estimation and equipment predictive maintenance, assuming the underlying technology is interchangeable. It’s not. The data structures, validation requirements, and failure modes are completely different. Treat them as separate problems.

The Hybrid Approach That Actually Works

The most successful implementations I’ve seen don’t replace traditional geostatistics with AI. They use both.

The typical workflow runs traditional kriging as the primary estimation method — it’s well-understood, auditable, and JORC-compliant — and then runs an AI model in parallel as a validation and enhancement layer. Where the two methods agree, confidence is high. Where they disagree, the geologist investigates.

This hybrid approach catches the cases where kriging smooths out real geological boundaries that the AI model, drawing on additional data types, identifies correctly. It also catches cases where the AI model produces geologically implausible estimates that kriging, constrained by simpler spatial relationships, avoids.

The geologist’s role doesn’t diminish in this workflow. If anything, it becomes more important. Someone needs to evaluate the discrepancies, understand what each method is telling them, and make the final call. That requires deep geological knowledge that no algorithm possesses.

Looking Ahead

The technology is improving rapidly. Within the next two to three years, I expect AI-enhanced estimation to become standard practice at large and mid-tier operations with sufficient data. The accuracy gains are too significant to ignore, and the tools are becoming more accessible.

But the path from promising pilot to production deployment isn’t straightforward. It requires clean, well-managed geological databases. It requires geologists who understand both geostatistics and machine learning. And it requires a realistic assessment of whether your deposit has enough data to train a useful model.

The ore bodies haven’t changed. Our ability to understand them is just getting sharper.