(and log-plotting, exports, and statistics, too)
There are a variety of filters that you can apply to your data prior to solid modeling if you wish to limit the data to be modeled. The program will ignore data that lies outside the declared range(s), omitting that data from the solid modeling process. These filters are available for I-data, T-data, P-data, and generic solid modeling tools. A subset of these are available for Lithology modeling.
Selected filters are also available for quantitative data exports, statistics and histogram summaries, and striplog columns.
Menu Options
- Spatial (XYZ) Filtering: Filter data points based on their location. These options are on the far right side of the modeling program's window. (More.)
- Filter Based on G-Values: Filter data points based on their measured value. (More.)
- Resample at Regularly-Spaced Intervals: Resample the data to regular depth intervals, with a variety of methods. (More.)
- Color Range Filter: Limit color modeling to input data within a specified range of a desired color. (More.)
- Create Filtering / Sampling Report: This provides an extensive listing of the settings and results, as shown here.
- Declustering: Insert a check here to activate control point Declustering, which can be helpful if you have clustered or duplicate points. It reduces the number of control points sent to the solid modeling algorithm by locating voxel spaces with multiple control points, computing their average, and sending that one point (snapped to the node location) to the modeling process. If only one control point resides within a voxel space, then it is sent to the modeling process as-is. Any control points outside the model space are not discarded. Once declustered, then RockWorks will model the new, smaller set of averaged points.
Declustering is useful when dealing with large data sets in which large voids would be overly influenced by clumps of dense control data. When modeling densely-sampled downhole geophysical or geochemical data, this can speed up the processing tremendously, with little degradation of data.
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