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Creating Interpolated Block Models and Grade Shell Meshes
bsomps edited this page Jan 4, 2025
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This operator uses Radial Basis Function (RBF) interpolation to create a volumetric block model from spatial data points. The workflow integrates with Blender's georeferenced 3D environment, allowing users to visualize interpolated scalar fields efficiently.
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.
-
Collection-Based Input
- Select objects from a chosen collection for interpolation.
- Extract spatial (
x
,y
,z
) and data (d
) properties automatically.
-
Bounding Box Extents
- Define interpolation boundaries using a bounding box object.
-
Grid Control
- Set grid size to control model resolution.
-
RBF Function Options
- Choose from multiple RBF kernels:
multiquadric
,inverse
,gaussian
,linear
,cubic
,quintic
, andthin_plate
.
- Choose from multiple RBF kernels:
-
Normalization and Outlier Control
- Normalize scalar field values and manage outliers using the Interquartile Range (IQR).
-
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.
-
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.
- Go to the GeoModeller Panel → Numerical Modelling → RBF Interpolated Block Model.
- Use the
Choose Collection
dropdown to select your data collection.
- 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.
- Select a numerical property (e.g.,
Zn_ppm
,Au_ppm
) from theData Property
dropdown.
- 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.
- Check
Normalize Colormap
to scale values based on IQR. - Adjust
IQR Scaling Factor
if needed.
- Click Generate Block Model to run the interpolation and visualization process.
- 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.
- Grid Size vs. Performance: Larger grid sizes significantly increase processing time.
- Outlier Handling: Use IQR normalization to minimize the impact of extreme values.
- Bounding Box Validity: Ensure all data points lie within the bounding box extents.
-
Console Feedback: Monitor progress and error messages in Blender's Console (
Window → Toggle System Console
).
- Data Validation: Ensure selected objects have consistent spatial and data properties.
- Bounding Box Adjustment: Keep extents tightly bound to the region of interest.
- Grid Optimization: Start with a lower grid size for testing, then increase resolution.
-
Batch Processing: Use Blender's
Batch Rename
tool to streamline object naming conventions.
For more advanced details about RBF interpolation, refer to the Scipy Documentation.