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GeoDict User Guide 2025

Binder Visualization

In the Binder Visualization tab you can import saved structures and computed volume fields which illustrate the binder identification process and its results.

GrainFind_IdentifyBinder_BinderVisualization

Load Structure with Binder

Click Load *.gdt to load the structure of the identified binder labeled with a different Material ID.

In the 3D visualization, the distribution of binder between the grains is visible for the whole domain. In the 2D view you can evaluate the result of the binder identification in more detail, as seen in the example below (top: original structure, bottom: with labeled binder).

Load Confidence Field

During the identification the solid voxels are separated into two classes, binder and grain based on the selected Threshold. The confidence field contains the unsegmented result of the binder identification, which means a probability for each voxel to belong to one of the classes. Select Load *.npz to view this volume file in GeoDict.

GrainFind_IdentifyBinder_BinderVisualization_NPZFields

Two fields are available, where the first field contains the probability for a solid voxel to belong to the first class (which is grain) and the second field shows the probability of a voxel to be binder.

Note-KnowHow

Know how! The values in the pore voxels are not used for the identification and can be ignored. Turn off the visibility of the confidence field in the pore voxels by disabling the visualization in the pore Material ID in the View Controls panel.

GrainFind_IdentifyBinder_BinderVisualization_NPZFields_DisablePoreID

For the visualization below, the second field channel_1:Numpy Field was loaded and you can observe easily where the neural network is confident of the choice and where it is uncertain: The dark blue areas are clearly marked as grain, the red areas are clearly marked as binder, and the values in between (bright blue to orange) are uncertain.

Note-KnowHow

Know how! If the segmentation results are not satisfying you can try another threshold and use the Identification Mode Load Neural Network Output.

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