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Types of block models

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The most common block model types used to define and manage the spatial distribution of geological and economic data are Regular, Percent, Subblocked, Reblocked, and GSM (Gridded Seam Model). Below is a brief overview and comparison of each type.

Block model types

  • Regular Block Model: The deposit is divided into uniform blocks of fixed size in three dimensions (X, Y, Z).

    This is the type natively supported by MiningMath. Formatting instructions can be seen here. Rotation of regular block models is also supported as described here.

  • Percent Block Model: Extends the regular model by incorporating percentage values to represent how much of a block contains a specific material or belongs to a given domain.

    This is not natively supported. However, instructions on how to format this model with and without dilution are provided here.

  • Reblocked Block Model: It is created by merging smaller blocks (often from a subblocked model) into larger ones to simplify calculations or meet specific optimization constraints.

    MiningMath provides an app in its MM Labs section that is able to reblock your block model. Further instructions can be seen here.

  • Subblocked Block Model: Refines regular blocks by dividing them into smaller sub-blocks where needed, typically along geological boundaries or to represent irregular shapes more precisely. It can give a higher detailed representation of geological features, but can also lead to large file sizes, higher computational demands, and higher complexity to prepare and manage.

    This is not supported by MiningMath.

  • GSM (Gridded Seam Model): This is used for stratified deposits, like coal or other layered materials. It divides the deposit into horizontal layers (seams) and models variations within each layer. However, it is less flexible for deposits with significant vertical or irregular variations.

    This is not supported by MiningMath.

Comparison and use cases

These models offer flexibility in balancing accuracy in deposit representation with complexity in computational effort required for optimization.

The table below highlights the trade-offs between accuracy and complexity, using the regular block model as a reference point. It also summaries the block model types supported by MiningMath.

Model type Best for Complexity (lower is better) Accuracy (higher is better) Support
Regular
Deposits with uniform geometry
Standard
Standard
Percent
Deposits with irregular ore/waste distribution
Higher
Higher
Reblocked
Simplified optimization or large-scale production planning
Lower
Lower
Subblocked
Detailed geological models with sharp boundaries
Higher
Higher
GSM
Layered, stratified deposits with predictable structures
Higher
Higher (for stratified deposits)
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