Using HyperCube – An AI approach to mineral exploration data integration for targeting

Traditional approaches to data integration for targeting typically focused on statistical classification models, employing numerous assumptions that are generally not met in practice. Recent, successful advances in stochastic, non-Euclidean approaches to the problem of understanding complex data relationships have been made in disciplines such as genomics and epidemiology. Predictive models are constructed from the integration of complex data sets without the limiting assumptions of traditional statistical approaches. These new approaches can easily handle continuous, discrete, noisy, and missing data without the imposition of statistical models or assumptions. In partnership with HyperCube Research, we are applying such an approach to the exploration targeting problem. The method provides a series of robust rules describing the relationship between input exploration data variables and mineral occurrences. The rules discovered are typically of much greater utility than statistical trend observations. These methods have the potential to improve discovery rates when applied as part of a carefully planned and systematic process of modelling, interpretation, and target generation.

Case study: Using predictive modelling in mineral exploration – Mount Dore area, AU 

This approach can be applied wherever conventional Weights-of-Evidence, logistic regression, neural networks, or other data-driven approaches would be appropriate. Hypercube analyzes relationships amongst many variables simultaneously in multi-dimensional data space rather than criteria by criteria. It removes the difficulties of determining “cut-offs” or thresholds for individual exploration criteria by replacing them with more interpretively useful multi-parameter “rules” driven by geological reasoning.

A few years ago, we carried out targeting work for IOCG-style mineralisation in the Mt Dore area of QLD, Australia. We produced a 3D model and predictive exploration map using the WofE approach for this regional scale project. Using the same data sets, we tested the power of predictive analytics. The results were much more useful. In the Hypercube result, at least one cell immediately proximal to all the known mineral deposits were identified within the upper 2nd percentile of the ranked prospectivity score. HyperCube ranks criteria by creating rules, which are sets of related criteria that define a phenomenon or event. The rules generated for the Mt. Dore model revealed clearly that training cells cluster into different groups which can be tied to subtle variations in geological settings.

On the left side is the WofE predictive exploration map and on the right side the HyperCube map. Numbers correspond to training data (known IOCG deposits), and red zones correspond to high prospectivity target selection areas. Note that fewer training sites are identified as high prospectivity zones in the WofE result (e.g. training sites 1, 7, 11). Other case studies we have looked at demonstrate that HyperCube presents fewer false positives. The HyperCube map is simply a much more focused prospectivity map.
©2010 Department of Natural Resources and Mines, Queenland. All rights reserved

John McGaughey – President

John is the founder and President of Mira Geoscience. He has extensive mining industry experience focusing on quantitative, multi-disciplinary 3D and 4D interpretation for mineral exploration and geotechnical decision support. He currently leads our technology strategy and our geotechnical business. Prior to founding Mira Geoscience in 1999, he spent 10 years at the Noranda Technology Centre as a Senior Scientist in their rock mechanics group. He obtained an MSc in geological engineering and a PhD in geophysics from Queen’s University. John is based in Montreal.

Latest news

Software releases
January, 15 2019

Geoscience INTEGRATOR AI for exploration

Geoscience INTEGRATOR, the missing AI link for exploration. This unique web-based data management system is designed...
Read more
Case studies
September, 01 2019

Machine learning in mineral exploration

We have applied machine learning as part of custom solutions to complex exploration and geotechnical problems since 2015...
Read more
Software tips
March, 01 2019

Create array property from scalar properties

This command, found in GOCAD's Property menu, creates a multi-dimensional array property from user-selected properties...
Read more
Software tips
February, 14 2020

Filtering data by neighbourhood

In the web UI, you can rapidly filter your data by neighbourhoods and display it in plots, charts, or tables.
Read more
Software tips
June, 06 2022

Coordinate system transformations

In Geoscience ANALYST Pro version 4.0, you can apply coordinate system transformations to...
Read more
Software tips
July, 01 2021

Easily display a property on multiple objects

In GOCAD Mining Suite objects are grouped by the properties they contain...
Read more
Geoscience ANALYST
July, 08 2021

Using ioGAS in Geoscience ANALYST Pro – Virtual Lecture

Past event, recording available here...
Read more
Software tips
September, 01 2022

Drillhole Statistics

In Geoscience ANALYST you can compute drillholes’ deviation statistics. These include...
Read more
Software tips
October, 01 2019

Display property values as text

Through GOCAD's Style Editor, property values in atomic objects can be displayed as text in the 3D Viewer...
Read more
Software releases
September, 24 2019

GOCAD Mining Suite upcoming release

Our next GOCAD release takes adventage of usability and improvements implemented by Emerson-Paradigm...
Read more
Software tips
January, 13 2022

New raster imports

In Geoscience ANALYST you can now import GeoTIFF (tif, tiff), ERMapper (ers), and Surfer (grd)... ..
Read more
Case studies
December, 05 2019

Rapid and accurate mapping

We explain how automated generation of alteration maps using radiometric data is well-suited to large-footprint mineral systems...
Read more

Please contact our team for additional information about our products and services