AI for Geological Exploration & Ore Body Modeling

Beyond Classical Geostatistics

Kriging has been the workhorse of grade estimation for decades — and it works. Ordinary kriging, universal kriging, indicator kriging — each has its place. But kriging assumes stationarity, relies on a well-fitted variogram, and produces smooth estimates that systematically understate grade variability. When your deposit has complex structural controls, multiple mineralization events, or sharp lithological boundaries, kriging's assumptions start to hurt.

ML-enhanced interpolation does not replace kriging. It augments it by learning the residual patterns that kriging misses — non-linear grade-lithology relationships, structural anisotropy that changes orientation with depth, and grade-thickness correlations that classical methods handle poorly.

The practical approach: run kriging as your primary estimator, train an ML model on the same data, and compare. Where the ML model systematically outperforms kriging in cross-validation, investigate why — you will often discover geological controls you had not modelled.

Drill Hole Data Analysis

Open data/drill-hole-data.csv in the code panel. Each row represents a downhole assay interval with collar coordinates, azimuth, dip, from-to depths, assay grades (Fe, SiO2, Al2O3 for iron ore; Zn, Pb, Ag for base metals; Cu, Au, Mo for porphyry systems), lithology codes, and core RQD (Rock Quality Designation).

Grade Estimation with ML

The classical workflow — compositing → exploratory data analysis → variography → kriging — is well established. AI enters at multiple points:

Automated lithology classification: Drill core logging is subjective. Two geologists will code the same interval differently 15-20% of the time. A classifier trained on assay geochemistry, magnetic susceptibility, and density can assign lithology codes consistently:

Features: Fe%, SiO2%, Al2O3%, LOI%, MnO%, P%, magnetic_susceptibility, density
Target: lithology_code (BIF, taconite, laterite, skarn, schist)
Model: Gradient Boosted Trees (XGBoost) — handles mixed scales, missing values

At Pilbara iron ore deposits in Western Australia (Rio Tinto, BHP, Fortescue), this approach achieved 92% agreement with senior geologist codes, compared to 83% inter-geologist agreement — and the consistency directly improved JORC resource classification confidence.

Core RQD correlation: RQD values from drill core correlate with rock mass quality but are noisy — they depend on core diameter, drilling method, and handling. A regression model trained on RQD, fracture frequency, weathering grade, and lithology can predict in-situ rock mass rating (RMR) more reliably than RQD alone. This feeds directly into slope stability analysis for open pit design.

Handling Sparse Data

Greenfield deposits and deep exploration extensions are often drilled on wider spacing than the 50m × 50m grids used near mature reserves — 100m × 100m or wider is common at early stages. AI helps here through transfer learning: train a model on densely drilled portions of the deposit, then apply it to sparsely drilled extensions with appropriate uncertainty quantification. Under NI 43-101 and JORC, that uncertainty quantification is exactly what separates an Inferred from an Indicated resource.

Geophysical Survey Interpretation

Open data/geophysical-survey.json — it contains gridded magnetic, gravity, and resistivity data from airborne and ground surveys.

Anomaly Detection

Traditional geophysical interpretation involves manual picking of anomalies on contour maps. AI-based anomaly detection automates this and finds subtle patterns that visual inspection misses:

Multi-Source Data Fusion

The real power comes from combining multiple geophysical datasets with geological mapping and drill hole data. A random forest or gradient boosted model trained on:

Inputs: magnetic_RTP, gravity_residual, IP_chargeability, resistivity,
        surface_geology, proximity_to_fault, proximity_to_fold_axis
Target: mineralization_probability (binary or continuous)

This produces a prospectivity map that quantifies exploration potential across the survey area. At a major iron ore deposit extension in the Hamersley Province, this approach identified two new drill targets that returned 58% Fe over 12m — targets that manual interpretation had classified as low priority.

Ore Body Modeling: 2D Sections to 3D Block Models

Open data/geological-map-features.json — it contains structural features (faults, fold axes, lithological contacts) digitized from geological maps.

AI-Assisted Block Modelling

The standard workflow: build a wireframe from interpreted cross-sections, fill it with blocks, estimate grades by kriging. AI improves each step:

Global Deposit Examples

Mineral sands (heavy mineral / monazite placers): Unlike hard rock deposits, mineral sand mineralization is controlled by sedimentary processes — sorting, wave energy, and heavy mineral concentration. The ore body is a thin, laterally extensive sheet. Block models must capture grade variability at sub-metre vertical resolution. AI-based geostatistical simulation generates multiple realizations that honour this thin-sheet geometry, critical for mine planning at deposits like Iluka's operations in Australia and the heavy mineral sand belts of the US Southeast.

Red Dog / Antamina-style base metal deposits: Among the world's largest zinc and polymetallic deposits, with complex folding and multiple ore lenses. Traditional sectional interpretation required 6-8 months for a resource update. Implicit modelling with ML-assisted boundary detection reduced this to 6-8 weeks while improving geological continuity of the model — and tightening NI 43-101 reconciliation against production.

Key Takeaways