Create 3D (XYZC) grids in Surfer

Analysis of 3D dimensional data such as water or soil contamination can be aided by the creation of 3D models.  3D grids generated from XYZC data enable the creation of 3D volumes, contours, slices, and more.

What is a 3D grid?

A 3D grid is a regularly spaced cuboid array of values where X and Y are the horizontal coordinates, Z is the vertical coordinate, and C is a value associated with each X, Y, Z location, commonly representing a property like temperature, concentration, or density.

How to create a 3D grid

The process for creating an XYZC grid in Surfer is very similar to creating a traditional XYZ grid file.  

1. Click Home | Grid Data | Grid Data.
2. In the Grid Data - Select Data dialog
   1. Click XYZC in the Data Type section.
   2. Select your preferred Gridding Method.
   3. Click Browse in the Dataset 1 field, select your XYZC data file and click Open.
   4. Select the X, Y, Z and C data columns and click Next.
3. In the Grid Data - Options dialog adjust the available parameters as needed for your data and click Next.
4. In the Grid Data - Output dialog
   1. Adjust the grid resolution, limits, and NoData settings as desired.
   2. Click the Change Filename folder in the Output Grid field to select a location and name for the file.
   3. Adjust the New layer type as desired and click Finish.

3D gridding methods

Surfer offers three XYZC gridding methods: Inverse Distance to a Power, Local Polynomial, and Data Metrics.

• Inverse Distance to a Power: The Inverse distance method is the most universal for all XYZC data distributions and is the default gridding method for in Surfer. 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.
  The inverse distance gridding 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 also allows the user to specify anisotropy, where the gridder can put weights on grid nodes in specific directions. 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.
  
  
• 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 recommended 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.