(035) New Feature (11/16/14/JPR): The Decluster option within the block-modeling dialog has been completely redesigned. Specifically, the program now uses a fast “octree” algorithm to remove duplicate points and to decluster clustered points. This method essentially creates a temporary block model in which a parallelopiped enclosing the control points is recursively subdivided into octants that contain control points until they are smaller than the specified maximum voxel voxel size. The program then looks for multiple occurances of control points within each of these voxels and replaces these control points with a single point based on the user-specified declustering method.
The declustering methods are described as follows:
- Average: The average G-value for all points within a voxel.
- Closest Point: The G-value for the point that is closest to the voxel midpoint. Recommended for modeling color and lithology.
- Distance Weighted: The estimated G-value based on an inverse-distance-squared weighting algorithm. Recommended for modeling most data sets except for color and lithology.
- Highest: The highest G-value for all points that reside within a voxel.
- Lowest: The lowest G-Value for all points that reside within a voxel.
The Horizontal Resolution defines the declustering voxel x-size and y-size as a function of the specified project dimensions. For example, if the Horizontal Resolution is set to 50% (the default) and the x-spacing for the project model is 100′, the horizontal size of a declustering voxel will be 50′.
The Vertical Resolution defines the declustering voxel height as a function of the specified project dimensions. For example, if the Vertical Resolution is set to 50% (the default) and the z-spacing for the project model is 2 meters, the vertical size of a declustering voxel will be 1 meter.
The Show Report option will display a dialog box (shown below) that summarizes how many points were consolidated via the declustering process.
In addition, this dialog provides an option to copy the declustered points to the RockWorks Utilities Datasheet (see below).
This data may be plotted in 3D by using the Utilities / Map / 3D-Points program (see below) to examine the effects of various declustering methods and resolution settings.
- As shown by the examples above, the declustering can speed up the processing by more than 50% (half the time!).
- Creating declustering voxels that are more than 50% of the project model voxel dimensions is not effective because we’re beginning to encounter a “point of diminished return” when the declustering is spending more time consolidating the points that reside within the declustering voxels. That’s why we don’t recommend declustering resolutions greater than 50% – you’re just losing accuracy and there’s no speed benefit.
- Declustering is turned on by default and set to the Closest Point method with a horizontal resolution of 50% and a vertical resolution of 50%.
- Although the dimensions of the temporary voxels are based the project dimensions node spacing, the octree model extents may extend the project dimensions in order to accomodate control points that reside outside the project dimensions.
- In addition to handling clustered points, the declustering will eliminate any duplicate points that are passed to the modeling algorithm. This is important because some of the modeling algorithms handle duplicate points poorly (i.e. producing divide-by-zero error messages).
- Data sets that are uniformly distributed do not gain a speed benefit from declustering. In fact, the declustering actually slows down the processing. For example, a data set with 50,000 randomly distributed control points (see below) required 330 seconds without declustering and 340 seconds (3% slower) with declustering.