Maptek DomainMCF


New paradigm for 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

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

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

Articles

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.

Case Studies

Modelling marble reserves

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

Conference Papers

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.

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.

 

Recognising the impact of uncertainty in resource models

This paper explores how measuring uncertainty in resource models provides mine planners and potential investors with a quantitative assessment of risk.