Maptek DomainMCF

A study of a haematite and magnetite deposit submitted for the Maptek Geology Challenge found that machine learning holds promise for grade control.

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.

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.

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.

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.

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.

AI and machine learning are transforming the way geologists and mining professionals approach modelling—but what does that mean for you? Join us as we discuss our machine learning-driven geological modelling solution, Maptek DomainMCF, and its future possibilities.

Flexible subscription access to machine learning for quickly generating domain models.

Caroline Burden, winner of the Geology Challenge for 2023, shares how she used Maptek geological modelling tools to control the complexity of her deposit.

The annual Maptek Geology Challenge is back! Hear from Senior Technical Lead, Richard Jackson how trying new approaches to geological modelling can benefit our industry and how you can get involved.

Machine learning offers tangible resource modelling benefits, including processing vast amounts of data and identifying patterns to help unravel complexity.

See how using machine learning to generate blocks models improves productivity through faster decision making. A case history demonstrates the way DomainMCF quickly generates models, allowing you to interrogate and analyse alternative interpretations.

Technical Lead Steve Sullivan chats about how the breadth of DomainMCF applications has surprised even its creators, less than a year after the machine learning engine’s release.

Maptek Senior Technical Sales Specialist & Technical Lead – DomainMCF Steve Sullivan presents Harnessing data complexity by using machine learning for rapid geological modelling for the AusIMM International Mining Geology Conference 2022.

Technical Lead, DomainMCF Steve Sullivan presents Recognising the impact of uncertainty in resource models.

Hear directly from Gretchen Moore, Senior Rock Mechanics Engineer at Sibanye Stillwater, on her experience using DomainMCF to create geological models from Diamond Drill Hole data.

From a small office offering geological database and plotting services, Maptek has grown to a global technology business with more than 20,000 users spanning 90 countries.

Deposits are complex by nature, but a lack of time and resources prevents us from interpreting their richness and complexity in full. This open-forum webinar discusses the modern domain modelling challenges geologists are facing today, and how harnessing technology is key to overcoming them.

Machine learning is already providing breakthroughs in rapid, accurate resource modelling. Learn how being transparent about uncertainty actually improves confidence. The recording includes the Q&A from two sessions.

Machine learning is already providing breakthroughs in rapid, accurate resource modelling. Now Maptek can reveal the unique ability to record the degree of uncertainty around resource predictions. 

Join this webinar to learn how being transparent about uncertainty actually improves confidence in your work. 

This webinar outlines the power and functionality of DomainMCF for resource modelling. A short video is followed by the Q&A session from the APAC-Americas session (from 16:05), and then the Europe, Middle East and African session questions (from 54:40).

DomainMCF is a new paradigm for domain modelling which applies machine learning to rapidly create resource models.

An expert panel featuring Penny Stewart, Hugh Sanderson and Christie Myburgh explores the issues that arise when we apply machine learning to mining applications. These include how we harness the vast volumes of data made available from mining processes, transform it into knowledge and apply that knowledge to continually improve. See more Maptek Forums 2020 here: www.maptek.com/forums/.

DomainMCF is an exciting new approach to resource modelling which takes advantage of machine learning to help geologists to quickly and accurately model geologic domains. Sites can incorporate new drilling and other exploration data into the operation faster than ever before.