3D Prospectivity Analysis

A data-driven approach to subsurface targeting and deposit modelling

A machine learning approach that consolidates drillhole data, geophysical layers and assay results into 3D prospectivity volumes, replacing simplified manual models with data-driven deposit geometry. Ideal for companies moving toward resource estimation that need a more rigorous and defensible picture of where mineralisation is most likely to occur at depth. 

Specialist Consulting Services

1. Geoscience integration & targeting 

   Bring together geological, geochemical, geophysical and drilling datasets into a more complete targeting framework. This helps teams identify relationships across datasets and supports more confident target prioritisation. 

2. Geophysical inversions

   Transform geophysical data into more interpretable subsurface models that help define geological structures, refine targeting and improve confidence in deposit geometry.  

3. Lithogeochemical & hyperspectral solutions

   Use lithogeochemical and hyperspectral datasets to improve geological interpretation, identify alteration patterns and support more detailed understanding of mineralised systems. 

4. 3D Modelling

   Build integrated 3D geological models that combine multiple datasets into a more coherent view of the subsurface, supporting interpretation, targeting and resource work.

5. Standardisation of logging data

   Improve the quality and consistency of legacy and current logging datasets so they are easier to compare, integrate and use in downstream modelling. 

6. Downhole data domaining

   Define geological or ore domains along the drill trace using structured downhole datasets, helping improve interpretation and strengthen model inputs.

7. Semi-automated core logging

   Support logging workflows with AI-assisted tools that improve consistency across high-volume drilling programs and help teams extract more usable geological information from core.

8. Core scanning data integration

   Combine core scanning outputs with geological logs, assays and other drilling datasets to improve interpretation and make better use of data collected during drilling.