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
    A kind of block model where each block has the same 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
    A kind of block model that allows blocks to be subdivided into subblocks, allowing for the efficient representation of finer-grainer detail within the model. Use SubblockedBlockModel to represent a block model with subblocks. See Subblocked Block Models for more information.

  • Sparse blocks models
    Similar to dense block models in that each block has the same size, but the model is sparse in that blocks are only defined at given locations within the 3D grid structure. Sparse block models allow for the more efficient storage of models that contain many holes or voids. Use SparseBlockModel to build a sparse block model. See Sparse Block Models for more information.

Which type of block model should I use?

Each block model type has advantages and disadvantages and is appropriate for different scenarios. For example, consider the following scenario. You want to generate a block model for data with the following properties:

  • 90% of the volume in the model extent requires block properties to be stored at one value for each 0.25 cubic metres.

  • 5% of the volume in the model extent contains nothing of interest (e.g. they are air above the area being modelled).

  • 5% of the volume in the model extent requires block properties to be stored at one value for each 1 cubic metre.

In this scenario, we would choose to represent the data using a dense block model. Subblocked models are only preferable if areas of high detail are relatively rare, allowing for the primary blocks to be larger. If areas of higher detail are common, the simpler dense block model provides a simpler model.

In contrast, consider another scenario. You want to generate a block model for data with the following properties:

  • 10% of the volume requires block properties to be stored 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 in the model extent requires block properties to be stored at one value for each 1 cubic metre.

In this scenario, we would use a subblocked block model to represent the data. This would allow high detail to be stored in areas of interest and low detail to be stored elsewhere without compromising file size.