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Maximizing Voxler's Gridder to Match Your Data

A frequently asked question in Voxler regards maximizing the gridding parameters to best fit a user’s data. Because data is so not uniform in its spatial distribution, there are no hard and fast rules on how to set up the gridding parameters to provide the anticipated results. Knowing a little bit about how the input data is spaced and how to effectively set-up Voxler’s gridding parameters to fit that data is the key to getting a good resulting lattice from the Gridder. This article will address setting up the gridding parameters for some sample down-hole soil data that exhibits dense data in the Z direction, but coarsely spaced in the X and Y directions.

Voxler: Down-hole well data represented by the Voxler WellRender and VolRender modules

General Gridding Information
The first step in maximizing Voxler’s gridder is to select the proper gridding method. Voxler offers three gridding methods: Inverse Distance to a Power, Local Polynomial, and Data Metrics. Each of these methods can be used for any data set to create a grid. But each method has certain areas where it is better than the others. Some good general information on these methods is included below.

  • Inverse Distance to a Power: The Inverse distance method is the default gridding method in Voxler and is the most universal for all data distributions. The Inverse distance method is fast but has the tendency to generate concentric spheres around high and low values unless you increase the Smooth value. This method does not extrapolate beyond the Z range of the data, so honors the actual minimum and maximum of the data set well. Inverse Distance allows the user to specify anisotropy, where the gridder can put weights on lattice nodes in specific directions.
  • Local Polynomial: The Local polynomial method is most applicable to data sets that are locally smooth, i.e., relatively smooth surfaces within the search neighborhoods. The computational speed of the method is not significantly affected by the size of the data set, so this is a good quick method for large data sets.
  • Data Metrics: The Data metrics method is used to calculate statistics about a data set. This gridding method is rarely used and is not recommend unless you have a specific purpose or project that requires it. If you need to know information about which data points (and the statistics on those data points) are used for each grid node, this is the method to use.

Gridding Example

To select one of the different gridding methods in Voxler, you first must have a Gridder module attached to a dataset. Once the Gridder is attached, the gridding method and properties can be altered.

  1. Click the File | Import command and import the data.
  2. Click on the data set in the Network Manager. Because we used well data, the data must be extracted. Click the Network | Computational | ExtractPoints command. If random data points had been imported in step 1, this step would not be necessary. This command converts the down-hole well data into random points.
  3. The default values will work for the ExtractPoints module. The output component could be changed, if we were modeling a different parameter from the well data.
  4. Click on the ExtractPoints module in the Network Manager.
  5. Click the Network | Computational | Gridder command to attach the Gridder module to the ExtractPoints module.
  6. To edit the gridding properties, click on the Gridder module in the Network Manager.
  7. In the Property Manager, click on the General tab.
  8. The gridding methods are listed next to Method. Because the Inverse Distance method has an Anisotropy property, which allows the user to weight the data points higher in a specific direction more than others, this is a good choice to grid soil data. Set the Method to Inverse Distance.

Setting the gridding method to Inverse Distance to a Power
in Voxler's Propery Manager.

Natural phenomena created by natural processes, such as soil horizons, typically have preferred orientations in the X and Y directions. If the Anisotropy is set to Anisotropic, an influence ellipse can be set to accommodate the data distribution. For soil data the X Length and Y Length can be set to approximately 10 to 100 times the Z Length values. With dense down-hole data, the Z Length values for the influence ellipse should be set to a relatively small value.

Setting the influence ellipse in Voxler's Property Manager.

Now that all the parameters are set, click the Begin Gridding button to grid the data. Once the data is gridded, click on the Gridder module in the Network Manager and click the Network | Graphics Output | VolRender command to create a 3D cube displaying the gridded data. By editing the properties of the Volrender, you can see inside the structure and examine how the different wells interact. Gridding properties can be changed and updated until the VolRender displays the 3D volume in the method you desire.

Conclusion

By using the above suggestions, you should be able to manipulate the gridding parameters for a fit that best represents your data.

If you have any questions, contact voxlersupport@goldensoftware.com.

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