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.

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

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

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

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

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

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

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

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

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