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
Geoscience ANALYST
January, 21 2020

UBC-GIF or VP Suite inversion?

"The programs are complementary, and the combination gives one the ultimate flexibility in potential-field inversion,” says Kristofer Davis, Scientific Programmer at Mira Geoscience.
Read more
Virtual Lecture – Archive
April, 08 2021

GOCAD Mining Suite – A geological modelling powerhouse

Past event, recording link available here...
Read more
Software tips
May, 03 2019

Documents linked to a project

When documents are linked to a project in Geoscience INTEGRATOR, it is possible to be more specific and link them to the project’s data sets...
Read more
Software tips
February, 03 2019

Organizing files as data sets

In Geoscience INTEGRATOR, a data set is often composed of a group of files of various formats along with metadata. These can be grouped...
Read more
Software releases
November, 16 2018

VPmg, VPem1D and VPutility release

VP Geophysics Suite releases: VPmg version 9.2, VPem1D version 4.2 and VPutility version 1.1...
Read more
Software tips
December, 14 2020

Tagging query results

In Geoscience INTEGRATOR, this allows you to rapidly access that subset of objects. Instead of having to search and select multiple filters every time...
Read more
Virtual Lecture – Archive
September, 10 2020

How to run gravity inversions in a geologically driven way

Past event, view here or on our YouTube Channel to see how to run a 3D inversion and forward modelling in Geoscience ANALYST Pro using VPmg...
Read more
Stratigraphic interpretation and modelling
March, 06 2020

Coal industry solutions

Our coal solution provides users the simplicity of 2D grid modelling with the geological complexity of 3D volumetric surface modelling...
Read more
Software tips
November, 16 2020

Themes

Geoscience INTEGRATOR v3.5 and Geoscience ANALYST v3.2 include new themes and renamed ones to help you to locate data in a more intuitive way.
Read more
Developer’s sandbox
September, 13 2013

R&D projects in data management

Over the past couple of years we have been developing skills and technology in practical, business-focused data management as a solid foundation for modelling, analysis, and interpretation.
Read more

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