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Creating Interpolated Block Models and Grade Shell Meshes

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bsomps edited this page Jan 4, 2025 · 13 revisions

RBF Interpolated Block Model

This operator uses Radial Basis Function (RBF) interpolation to create a volumetric block model from spatial data points. The block model operator is very basic in its functionality, many limitations exist and it is not recommended for any resource estimation at this stage.

RBF Interpolated Block Model Example

Overview

The RBF Interpolated Block Model operator allows users to:

  • Select a collection containing objects with spatial data and custom properties (numerical attributes).
  • Define a bounding box object to set interpolation extents.
  • Choose data properties (e.g., geochemical values) for interpolation.
  • Customize grid resolution, interpolation functions, and normalization settings.
  • Visualize results as a structured volumetric block model organized into collections.

Key Features

  1. Collection-Based Input

    • Select objects from a chosen collection for interpolation.
    • Extract spatial (x, y, z) and data (d) properties automatically.

  2. Bounding Box Extents

    • Define interpolation boundaries using a bounding box object.

  3. Grid Control

    • Set grid size to control model resolution.

  4. RBF Function Options

    • Choose from multiple RBF kernels: multiquadric, inverse, gaussian, linear, cubic, quintic, and thin_plate.

  5. Normalization and Outlier Control

    • Normalize scalar field values and manage outliers using the Interquartile Range (IQR).

  6. Dynamic Visualization

    • Generate a structured block model organized into 10 collections based on interpolated values.
    • Apply color gradients derived from Matplotlib's Spectral_r colormap.

Input Data

  • Collection: Choose the collection holding your data objects (can be mesh objects or curve objects) must have valid spatial (x, y, z) and numerical (data_property) properties.
  • Bounding Box: A Blender object (e.g., cube) defines the model extents.
  • Data Property: Select the numerical property for interpolation from the custom object properties.

Step-by-Step Guide

1. Select Input Collection

  • Go to the GeoModeller Panel → Numerical Modelling → RBF Interpolated Block Model.
  • Use the Choose Collection dropdown to select your data collection.

2. Set Bounding Box

  • Create a cube object in the scene.
  • Position and scale it to enclose the area of interest.
  • Select this cube in the Bounding Box Object dropdown.

3. Choose Data Property

  • Select a numerical property (e.g., Zn_ppm, Au_ppm) from the Data Property dropdown.

4. Configure Grid and Interpolation

  • Set Grid Size to control interpolation resolution (higher values = finer grid).
  • Choose an RBF kernel from the RBF Function menu.
  • Adjust Epsilon Value or leave it to auto-calculate average point distance.

5. Enable Normalization (Optional)

  • Check Normalize Colormap to scale values based on IQR.
  • Adjust IQR Scaling Factor if needed.

6. Generate the Block Model

  • Click Generate Block Model to run the interpolation and visualization process.

Visualization and Output

  • Structured Collections: Blocks are grouped into 10 collections based on scalar field value ranges.
  • Object Properties: Each block retains spatial (x, y, z) and interpolated value (d) data.
  • Color Mapping: Colors are assigned dynamically using Spectral_r colormap.

Notes and Limitations

  • Grid Size vs. Performance: Larger grid sizes significantly increase processing time. Any grid size over 25 will take a considerable amount of time and result in laggy blender scenes.
  • Outlier Handling: Use IQR normalization to minimize the impact of extreme values.
  • Bounding Box Validity: All data points do not need to lie within the bounding box extents. The bounding box will clip the data to what it encompasses.
  • Console Feedback: Monitor progress and error messages in Blender's Console (Window → Toggle System Console).

Tips for Efficient Modeling

  1. Data Validation: Ensure selected objects have consistent spatial and data properties.
  2. Bounding Box Adjustment: Keep extents tightly bound to the region of interest.
  3. Grid Optimization: Start with a lower grid size for testing, then increase resolution.

For more advanced details about RBF interpolation, refer to the Scipy Documentation.

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