Metals AI

AI Geologic Modeling

Create a beautiful, realistic geologic model from your data in minutes. Using anything you have available- from structures to geology, core logs and photos- Metals AI will attempt to rapidly resolve a geologic model that fits your data and informed by real life case studies and physical rock properties.

In mineral exploration and geological modeling, one of the biggest challenges is constructing accurate 3D block models when data is limited. Traditional methods rely on extensive drilling and manual interpolation, which can be costly and time-consuming. However, advancements in artificial intelligence (AI) and machine learning (ML) are transforming this process, allowing geologists to generate geologic block models from sparse data and 2D geological maps with unprecedented accuracy.

At the core of these advancements is rigorous validation using academic research and published systems research, ensuring that AI-generated models are geologically sound, reliable, and aligned with real-world geological processes.

Our goal: Leverage AI to Generate Geologic Block Models from Sparse Data and 2D Maps

The Challenge: From Sparse Data to a 3D Model

Creating a 3D geological model typically requires a combination of borehole data, geophysical surveys, and surface mapping. However, in many early-stage exploration projects, data is sparse, making it difficult to interpolate subsurface structures with confidence. Traditional interpolation methods, such as kriging or inverse distance weighting (IDW), often fail to capture complex geological relationships, leading to oversimplified models.

How AI Enhances Geological Modeling

AI-powered modeling approaches overcome these challenges by learning geological patterns from existing datasets and intelligently predicting subsurface structures. Our approach combines state-of-the-art AI techniques with established academic research in geostatistics, computational geology, and structural geology to ensure accuracy and reliability.

1. Data Integration and Preprocessing

AI models can ingest and process multiple data types, including:

  • 2D geologic maps (structural trends, lithology, and fault networks)
  • Surface geochemisty (soils, rocks)
  • Surface spectral imagery (captured locally or using our Satellite tool)
  • Drillhole data (lithology logs, geochemistry, and geophysics)
  • Geophysical surveys (gravity, magnetics, and seismic data; inversions of these using our Geophysical Inversion tool)
  • Remote sensing imagery (satellite and LiDAR data)

We incorporate peer-reviewed geological models and validated geophysical inversion techniques to ensure data integration aligns with known geological frameworks. Published case studies and comparative analyses help benchmark AI-driven data processing against traditional methods.

2. Machine Learning for Predictive Modeling

Our AI-driven geological modeling leverages cutting-edge machine learning techniques validated through academic research in geostatistics and computational modeling:

  • Neural networks (CNNs & GANs): Used to recognize patterns in 2D maps and extrapolate them into 3D space. These are trained on large datasets from published geologic case studies.
  • Gaussian Process Regression (GPR): Applied for predicting rock unit continuity and quantifying uncertainty, supported by published works in probabilistic geostatistics.
  • Geostatistical AI models: Hybrid approaches integrating ML with traditional geostatistics (e.g., variography, exploratory factor analysis) to improve interpolation accuracy, based on methods from leading research institutions.

3. Model Validation Using Published Research & Field Data

A key advantage of our AI approach is its validation against peer-reviewed geological studies and field-tested models. Our validation process includes:

  • Comparison with established geological models: AI-generated block models are cross-referenced with published geological maps and known regional structures.
  • Integration of geophysical constraints: AI-assisted gravity and magnetic inversion results are tested against published inversion case studies to ensure geophysical consistency.
  • Bayesian optimization: Model predictions are refined based on probabilistic methods cited in geostatistics and geological uncertainty research.
  • Real-world validation: AI-predicted models are continually compared against actual drilling results, with feedback loops informed by academic studies on model uncertainty.

By using a combination of peer-reviewed geoscience literature, industry-standard methodologies, and real-world field validation, we ensure that our AI models are not just predictive, but also scientifically robust and geologically meaningful.

Benefits of AI-Generated Geologic Block Models

  • Faster Model Generation: AI significantly reduces the time needed to create a preliminary geological model.
  • Cost Efficiency: By maximizing the use of existing data, AI helps minimize costly drilling campaigns.
  • Improved Accuracy: AI-based models capture complex geological relationships better than traditional interpolation methods.
  • Scientific Rigor: Models are continuously improved based on published geological research and academic validation.
  • Enhanced Decision-Making: AI-powered uncertainty analysis helps exploration teams prioritize high-potential targets.

Future of AI in Geological Modeling

As AI continues to evolve, its integration with academic geoscience research, published geophysical case studies, and cutting-edge computational methods will drive the next generation of geological modeling. By leveraging peer-reviewed methodologies and real-world validation, AI-driven geological modeling is set to revolutionize exploration geology, reducing risks, improving discovery rates, and unlocking new mineral resources.

Want to leverage AI for your exploration projects?

Contact us to learn how AI-driven geological modeling, backed by academic research and real-world validation, can enhance your mineral exploration strategy.