Metals AI

Resource Modeling

AI-powered resource modeling can rapidly transform your precious and base metal exploration targeting in real time. Model any metal, chemistry, mineral or fingerprint: our tools will generate and update drill targeting solutions to be used as soon as your drill team hits ‘enter’ on the keyboard.

In the mining industry, accurate resource modeling is crucial for assessing the economic viability of a deposit. Traditional modeling methods rely on extensive drilling, manual data interpretation, and geostatistical techniques, which can be time-consuming and expensive. However, artificial intelligence (AI) is revolutionizing resource estimation by improving accuracy, efficiency, and predictive capabilities—helping mining companies unlock value faster.

Whether targeting gold, silver, copper, nickel, or zinc, AI-driven resource modeling enhances geological interpretations and optimizes exploration strategies. Here’s how.

The Challenges of Resource Modeling

Resource modeling for precious and base metals depends on a range of data sources, including:

  • Drillhole assays and lithology logs
  • Geophysical surveys (magnetics, gravity, and EM)
  • Geochemical anomalies
  • Structural geology data
  • Remote sensing and hyperspectral imagery

The challenge lies in integrating these datasets and predicting ore distribution in areas with limited drilling. Traditional interpolation methods, such as kriging, work well in data-rich environments but can struggle with complex orebody geometries and sparse datasets.

This is where AI-powered modeling excels.

How AI Enhances Resource Estimation

AI uses advanced machine learning techniques to detect patterns, predict mineralization zones, and generate more accurate 3D resource models. Here’s how it works:

1. Data Integration and Feature Engineering

AI can process and integrate multi-source geological data more efficiently than traditional methods. By using machine learning algorithms, we can:

  • Standardize and clean drillhole and assay data
  • Identify correlations between geophysical anomalies and metal grades
  • Extract structural and geochemical patterns that indicate mineralization trends

This allows geologists to identify hidden relationships in the data that might be missed using conventional techniques.

2. Machine Learning for Orebody Prediction

Once trained on existing drillhole data, AI models can predict orebody extensions and define mineralization zones using:

  • Supervised learning (Random Forest, XGBoost, Neural Networks): AI learns from known ore intersections and predicts mineralization in unexplored areas.
  • Unsupervised learning (Clustering & Anomaly Detection): AI detects geochemical and geophysical patterns associated with economic mineralization.
  • Deep learning for complex orebodies: Neural networks help model intricate vein-hosted gold systems or disseminated copper porphyry deposits.

3. AI-Driven 3D Geological and Resource Models

AI-generated 3D models provide a more refined and geologically meaningful representation of the deposit by:

  • Reducing uncertainty in grade distribution
  • Enhancing orebody geometry predictions
  • Optimizing pit shell designs and underground mining plans

Unlike traditional block modeling, AI can dynamically update models in real time as new drilling data becomes available, enabling more adaptive decision-making.

Case Study: AI in Copper-Gold Exploration

In a recent copper-gold project, AI-assisted resource modeling reduced drilling costs by 30% while improving orebody delineation. By integrating geological, geophysical, and geochemical data, the AI model accurately predicted high-grade zones, leading to more efficient targeting and higher discovery success rates.

The Benefits of AI-Powered Resource Modeling

✅ Faster Resource Estimates – AI accelerates the modeling process, reducing time from exploration to feasibility studies.
✅ Lower Drilling Costs – AI identifies priority drill targets, minimizing unnecessary drilling expenditures.
✅ Improved Accuracy – AI-driven models reduce estimation errors and enhance confidence in resource classification.
✅ Better Decision-Making – AI continuously refines predictions as new data is collected, improving mine planning and investment strategies.

The Future of AI in Resource Modeling

AI is set to play an increasingly critical role in mineral exploration, helping companies improve discovery rates, reduce costs, and optimize extraction strategies. As machine learning algorithms become more sophisticated and data availability increases, AI-powered resource modeling will drive the next generation of mining projects.

Looking to optimize your resource modeling?

Contact us to learn how AI can improve your precious and base metal exploration strategy and enhance your resource estimation accuracy.