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
February, 10 2020

Fourier filters

This new Geoscience ANALYST Pro feature allows you to apply Fourier filters on 2D grids using the basic geophysics tools...
Read more
Developer’s sandbox
October, 28 2020

New clustering application in geoapps

geoapps - Exploratory Data Analysis (EDA), rock classification generation, or map alteration footprints
Read more
July, 26 2021

Q&A: Revival Gold Inc.

We discussed Mira Geoscience’s contribution to further understand the Beartrack-Arnett gold project area using modern exploration techniques...
Read more
About us
June, 21 2019

20 years of modelling the earth!

Mira Geoscience marks its 20th anniversary championing integrated interpretation for better earth modelling, interpretation, and data management...
Read more
Developer’s sandbox
June, 08 2020

Advanced drillhole planning and monitoring

We partnered with Groupe MISA to develop a low-cost 3D application where all elements of drillhole design and monitoring...
Read more
Software tips
December, 01 2020

Editing property transparency

In GOCAD Mining Suite, when you select a property its transparency can be edited...
Read more
November, 22 2019

Q&A ERO Copper Corp

We discussed Mira Geoscience’s contribution to further understand the Mineração Caraíba project area using modern exploration techniques...
Read more
Software tips
November, 04 2021

Pointer Tracking

In GOCAD Mining Suite you can take advantage of the Pointer Tracking view to track coordinates, objects, and properties.
Read more
Software tips
July, 05 2022

Object’s Info

In GOCAD Mining Suite you can see how many nodes or triangles your Surface object consists of by right-clicking on...
Read more
Software tips
December, 07 2020

Automatic rotation

In Geoscience ANALYST you can autorotate viewport when presenting your 3D geoscientific data and models...
Read more

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