Block Models

Block models are three-dimensional models made up of smaller units called blocks. There are three kinds of block models supported by the Maptek Python SDK, as follows:

  • Dense block models
    Dense block models are composed of blocks of uniform size. The model is dense in that a block is defined at every location within the 3D grid structure over the model’s extent. Use DenseBlockModel to represent dense blocks. See Dense Block Models for more information.

  • Subblocked block models
    Subblocked block models allow for blocks to be subdivided into smaller subblocks, which enables a more efficient representation of fine-grained details within the model. This approach is ideal for scenarios where varying levels of detail are required. Use SubblockedBlockModel to represent a block model with subblocks. See Subblocked Block Models for more information.

  • Sparse blocks models
    Sparse block models, like dense models, feature blocks of uniform size. However, in sparse models, blocks are only defined at specific locations within the 3D grid structure, allowing for efficient storage when the model contains numerous voids or empty spaces. Use SparseBlockModel to build a sparse block model. See Sparse Block Models for more information.

Choosing the right block model

Each type of block model offers distinct advantages and is suitable for different use cases. Consider the following scenarios to determine the best model for your needs.

Scenario 1:

You need to generate a block model for data with the following characteristics:

  • 90% of the model's volume requires block properties at one value per 0.25 cubic metres.

  • 5% of the volume contains nothing of interest (e.g. air above the area being modelled).

  • 5% of the volume requires block properties at one value per 1 cubic metre.

In this scenario, a dense block model would be the most suitable choice. Dense models are preferable when high-detail areas are common, as they provide a simpler and more straightforward structure. Subblocked models are advantageous only if high-detail areas are relatively infrequent, allowing the primary blocks to be larger.

Scenario 2:

You need to create a block model for data with the following characteristics:

  • 10% of the model's volume requires block properties at one value per 0.25 cubic metres.

  • 20% of the volume contains nothing of interest (e.g., air above the area being modelled).

  • 70% of the volume requires block properties at one value per 1 cubic metre.

For this scenario, a subblocked block model would be more effective. It allows for high-detail storage in areas of interest while maintaining lower detail elsewhere, optimising file size and efficiency.