Create a DomainMCF Model

The tools for working with DomainMCF are all located on the DomainMCF ribbon tab.

DomainMCF models are generated by bundling your input data and model parameters into processing units called Jobs. When you start a job, the data is uploaded to the Maptek Compute Framework (MCF) for processing in the cloud. The MCF uses machine learning techniques to predict domain boundaries based on the input data and generates a block model. When processing is complete, you can download and view the model in Vulcan GeologyCore.

Get started using DomainMCF modelling by following these steps:

  1. Launch the DomainMCF Jobs tool.

    The DomainMCF Jobs tool allows you to manage all of your DomainMCF jobs.

    To launch the tool, click DomainMCF Jobs on the ribbon.

    The DomainMCF Jobs panel appears.

    From this panel you can configure and submit new jobs, as well as view summaries of previous jobs.

  2. Configure a job.

    A DomainMCF job starts off as draft, which means it has not been submitted for processing yet.

    The first time you open the DomainMCF Jobs panel, a new draft is created for you ready to fill out. Subsequently, if there are existing jobs in the list, you’ll need to start a new job by clicking New Job.

    One you have a draft job, you need to supply a set of inputs. Each input has a status icon next to it, as follows:

    Input incomplete
    Input complete
    Input complete with warnings
    Input invalid
    Input incompatible

    Configure the inputs as follows:

    1. Optionally, rename the job in the Job Name field.

    2. Specify the DomainMCF machine.

      DomainMCF Machine files (.dmcf_machine) can now be imported into the DataEngine. These act as file references to a machine file on the file system. A machine file must be imported before it can be dragged into the DomainMCF job panel.

      There are two ways to specify DomainMCF machine:

      Import existing machine - drag and drop the existing DomainMCF machine into the field. It will load the sample data and its corresponding variables.

      Train machine from sample data- specify sample data.

      The sample data is a set of points that provide the known domain values. DomainMCF uses these known points to build a model, which is then used to estimate the domain at any given point within the block model extent.

      In the Sample Data field, specify either:

      • One or more drillhole databases . Vulcan GeologyCore will automatically extract the sample points from the drillholes as interval midpoints. If you specify a database, all drillholes in the databases specified will be used.

      • A selection-group object containing drillholes as input. This is treated the same way as a drillhole database in which the holes contained are used to extract a point set for modelling.

      • A set of sample points . This can be obtained by importing a text file containing comma-separated values (CSV). For more information, please refer Creating a new DomainMCF machine.

        You can also generate a set of sample points by running the Extract Points option on a drillhole selection. Do this if you wish to model multiple domain fields in the same model, or use grade to help predict domains.

        Note:  Alternatively, you can drag and drop multiple files onto the Sample Data field, and the combined sample points will automatically be extracted.

      • Combine multiple point sets .

        You can correlate and combine multiple sample points by running the option Correlate and Combine points. Use this option when you have multiple point sets that you wish to upload to the DomainMCF.

    3. Specify a block model definition.

      A block model definition contains the physical configuration of the desired block model, including its origin, extent, orientation and block size constraints, as well as the names of the variables you want to model.

      You can either supply an existing block model definition, or let Vulcan GeologyCore generate one for you based on your sample data.

      • To use an existing block model definition , drag it from the project explorer into the Block Model Definition field. Block model definitions are stored in the block model definitions container by default. To import a block model definition from a Vulcan block model definition file (*.bdf), go to HomeData Import.

      • To generate a block model definition from your sample data, simply click to the right of the Block Model Definition field. The generated block model definition will be created in the block model definitions container , named after the job name, and suffixed with “BDF”.

      Note:  DomainMCF accepts regular block models (having all blocks the same size), and sub-blocked block models with a parent to sub-block ratio of ½, ¼, ⅛ or 1/16. Imported block model definitions will be rounded to the nearest size compatible with DomainMCF.

      Whether you supply an existing definition or generate one, you can inspect and modify the parameters by clicking to the right of the Block Model Definition field.

    4. Specify limits to constrain the size and shape of the realised block model. These are optional surfaces or a solid file that are used to limit the resulting block model to a specific region in space. This is useful, for example, to prevent predicting points in the sky (by supplying a surface representing the topography), or to restrict modelling to a certain subregion of the block model extent.

      Select from the drop-drop list either:

      • None (default) — doesn't specify any limit.

      • Surfaces — represents top and bottom topographic surfaces. Drag and drop the surfaces into the top and bottom fields. The realised block model will be confined to the region between the two surfaces.

      • Solid — the realised block model will be confined to the region inside the solid. Drag and drop it into the solid field.

        Specifying a convex hull is a good way of limiting the generated model to the volume covered by the sample data.

    5. Optionally, you can choose to select these two options to configure Block model output.

      • Report distance from block centroid to nearest sample point — If you select this checkbox, the block model will include a sample point distance attribute for each block. Note that this option can increase the time taken for the job to run.

      • Set maximum distance — You can select this option if the Report distance from block centroid to nearest sample point checkbox is selected. If selected, the sample point distance will be calculated up to the specified value. If not selected, an exact distance will be calculated for each predicted block. Specifying a lower value can reduce the computation time.

    Once all the inputs are valid , the Review Job button becomes enabled, meaning you can check if the job is ready for processing or not.

    Once Review Job is run, the following panel is displayed which gives information about the DomainMCF machine and Block model output to be processed.

    Note the Available balance of processing time you have left, and the Estimated compute time for the job.

  3. Run the job.

    To submit your job for processing, click Start Job.

    Your job begins uploading to the MCF server for processing, and the DomainMCF Job tool displays a summary of the job details.

  4. Download and view the result.

    When processing is complete, the resulting block model will be automatically downloaded and will be stored in the block models container .

    You can click View Block Model to load the block model into a new view window.

    To colour the block model, right-click on it in the view, and in the context menu select Colour By[Domain Attribute].

Create an Implicit Model

Create a Stratigraphic Model