Geological modelling and resource estimation guides
GEOREKA guides explain one geological modelling or resource estimation question each, answered completely in the first two paragraphs and then worked through with examples.
Start with the guide that matches your question. Each one links to the GEOREKA feature that applies the method, so you can move from the idea to doing it on your own data. New to estimation? Read the four-part introduction in order.
Start here
Introduction to resource estimation: a four-part series
Resource estimation explained
Part 1. From a simple average to a weighted average, with no maths beyond one formula.
From kriging to machine learning
Part 2. How kriging, RBFs, SVMs and neural networks each find their weights.
Kriging, RBFs and ML mathematics
Part 3. The shared mathematics of kriging, dual kriging, RBFs and machine learning.
The covariance function
Part 4. Variograms, kernels and RBFs are the same idea, and how to choose one.
More guides
Domains, machine learning and visualisation
Signed distance functions
What implicit modelling really estimates, and why SDFs are flexible and fast.
Domains from drillholes
How logged categories become signed distance functions, and why youngest-first order sets domain relationships.
Soft and hard domain boundaries
When to estimate each domain on its own samples, and when to let samples cross the contact.
Machine learning in geology
Neural networks, SVMs and Gaussian processes, with cautions and recommendations.
Multi-element clustering
Score every sample for similarity across all elements and position to help define domains.
Visualising mineralisation trends
How to display drillhole data in 3D without creating false trends.
Estimation in practice
Anisotropy, validation, uncertainty and classification
Local varying anisotropy
Why one search ellipsoid is a compromise, and how a local anisotropy field follows the structure.
Validating a resource estimate
Cross-validation, swath plots, statistics and grade-tonnage checks, and what each can and cannot show.
Conditional simulation
Why estimates smooth, how turning bands simulation measures uncertainty, and what to do with the results.
Resource classification methods
Distance to drilling, simulation precision and Nyquist sampling adequacy, and when each applies.
Automating modelling with Python and MCP
Script repetitive modelling steps, or let an AI assistant drive them, while you keep the interpretation.
Coming next
Guides in preparation
- What is a block model?
- Drillhole compositing
- Geological cross-sections