Maptek DomainMCF

Machine learning assisted domain modelling

Maptek DomainMCF uses machine learning to generate domain boundaries direct from sample data for rapid creation of resource models. Geologists feed in drilling or other sampling data and obtain domain or grade models in dramatically less time than traditional resource modelling methods.


The Maptek Compute Framework moves away from complex software installed on desktop computers and shifts costs from capital to operating budgets. Faster, secure cloud processing is supported by flexible licensing.

Generate resource models in minutes or hours, depending on size, data density and complexity of the deposit.

Many operations only have the ability to update their resource models once or twice a year. Using DomainMCF, your project can be modelled in minutes with results comparable to classical techniques. You remain in control of the process without the onerous preparation work. New data can be added and models regenerated quickly to reflect the current data.

Streamline workflow - data errors are highlighted and the modelling job cannot be submitted until changes have been made to remedy these.

DomainMCF solves another challenge - having valid geological data to input into the resource model. Data validation is completed within DomainMCF before the modelling phase.

The new geological domain modelling process using machine learning is especially suited to large volumes of data such as later stage exploration projects and operating mines. Your operation will benefit from multiple solutions run in parallel using high performance cloud computing.


Benefits

Improve productivity
Produce resource models up to 2000 times faster and more cost effectively than other solutions.

Maximise investment
Generate resource models with certainty and report investment options to stakeholders.

Reduce costs
Cloud based processing and machine learning save time for resource modelling.

Standardised process
Repeatable and reliable results with the ability to easily update resource models.

Manage risk
Identify potential projects and accurately interpret the volume of geological data for targeting high value projects.

Measure uncertainty
Provide mine planners and potential investors with a quantitative assessment of risk due to geological uncertainty.

Build on success
Apply professional expertise to interpretation and evaluation, supported by automated machine learning approach.

Minimal setup
Cloud processing solution without onerous start-up or customisation. Start generating models within minutes.

Features

 
Introducing Maptek DomainMCF
  • Automated modelling process
  • Validate data before modelling
  • Update models to reflect current data
  • Output domain and grade codes
  • Model grade trends
  • Evaluate potential projects
  • Effective for most deposit types
  • Fast, secure processing

Apply cloud processing and machine learning to generate accurate resource models with DomainMCF

Mine Geologists

  • Rapidly conduct grade estimation and feed results to standard grade estimation
  • Standardise on consistent grade control process
  • Easily update geological models with new data
  • Avoid time-consuming data manipulation
  • Single workflow helps eliminate data handling errors

Project Evaluators

  • Rapidly analyse and model geological data
  • Evaluate various scenarios to better inform project decisions
  • Reduce time and costs associated with project evaluation
  • Avoid time-consuming interpretation of data
  • Standardise and streamline process for project evaluation

Learn more about DomainMCF

DomainMCF Articles

Confident, controlled domaining

Maptek will introduce new audit controls and confidence measures for geologists, with a range of persistent model features taking the spotlight in DomainMCF in 2023.

Alternative geological interpretations

Maptek DomainMCF provides a data-driven process to generate alternative models to satisfy JORC reporting standards.

Harnessing deposit complexity

Machine learning techniques use all data sources for geological modelling, enhancing the understanding of complex deposits and improving decision making.

Data driven geology

Technological advances have matured machine learning to a point where it can be readily and practically applied by solutions such as Maptek DomainMCF.

Uncertainty in domain modelling

Generating models that include a measure of uncertainty leads to better informed decision making and compliant resource statements.

Reaching the rapid modelling goal

Maptek has developed a new solution for evaluating projects, progressing from database to resource report in 30 minutes.

DomainMCF Case Studies

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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.

DomainMCF Conference Papers

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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.

Webinars & Videos

October 3, 2023

Meet the Geology Challenge 2023 winner

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

August 2, 2023

Controlling Complexity

Create fast, accurate geological models using DomainMCF, without the need to set up the relationships of all units, while maintaining control.

July 13, 2023

Maptek Geology Challenge 2023

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.

April 4, 2023

DomainMCF

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

September 30, 2022

How DomainMCF can improve mine operations

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.

April 27, 2022

Value discovered with DomainMCF

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.

March 31, 2022

Harnessing data complexity

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.

September 19, 2021

Recognising the impact of uncertainty in resource models

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

April 7, 2021

Geological Modeling using Machine Learning in a Production Environment

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.

March 5, 2021

Maptek 40 years timeline

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.

March 4, 2021

Harnessing deposit complexity

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.

November 25, 2020

Towards greater certainty in resource modelling

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.

November 24, 2020

Towards greater certainty for resource modelling

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. 

July 27, 2020

Machine learning for resource modelling

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).

July 6, 2020

Introducing Maptek DomainMCF

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

June 15, 2020

Why Machine Learning

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/.

June 15, 2020

A new paradigm for domain modelling

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.