All Case Studies

Advancing with DomainMCF

An exploration company in Canada found that Maptek domain modelling technology provided
a faster way to uncover the resource potential of a gold project.

Quantifying geological uncertainty

A simulation study that tested alternative methods of modelling intrusive pegmatites in a nickel
sulphide deposit affirmed the value of Maptek DomainMCF.

Using AI to find copper

Maptek machine learning application DomainMCF helped a copper miner plan infill drilling for its project in South Australia.

Seeing patterns in geology structures

An engineering geologist outlines the challenges of revisiting a gold deposit and how Maptek modelling tools were applied to control the complexity.

From challenge comes opportunity

For a New Zealand consulting firm, the Maptek Geology Challenge was a chance to trial new software that could ultimately improve outcomes for clients.

Improving on traditional modelling

Machine learning techniques trialled alongside traditional resource modelling at an underground metals mine demonstrates future benefits.

Data driven modelling in a production environment

DomainMCF can change the way an operating mine uses geological and geotechnical models to keep information up to date in a production environment.

Modelling marble reserves

Applying machine learning to model marble reserves resulted in faster results and more uniform quality classifications to guide extraction.

Machine learning for fault identification

Machine learning engine for domain modelling is able to identify faulted geology in record time.

Domain modelling delivers accurate results

The new Maptek geological domain modelling process employing machine learning has delivered accurate shapes and volumes in much shorter time.

Deep Learning – A New Paradigm for Orebody Modelling

This 2019 paper introduces you to orebody modelling of the future. Learn how machine learning allows rapid generation of domained orebody models, including the estimation of multiple numeric variables and uncertainties, directly from drillhole data.

Harnessing Data Complexity – How Machine Learning Applies All Project Data for Accurate Resource Modelling

This paper discusses the factors accentuating complexity in deposit modelling: data diversity, structural controls, chemistry, data volumes, process workflows and external non-geological constraints. A case study illustrates the risk of ignoring complexity, which can result in an overly simplified geological model.

Application of Machine Learning to Resource Modelling of a Marble Quarry

This 2021 paper outlines the DomainMCF modelling approach to the spatial distribution of marble quality categorisation parameters and compares it to marble product classifications generated by the conventional estimation method.

Machine Learning in Resource Geology – Why Data Quality is Critical

This paper outlines the results of the Nova-Bollinger modelling trial conducted by the mine geology team for mineral resource estimation. Advantages and disadvantages of implicit modelling and machine learning methods for preparing mineral resource estimation domains are covered.