Interpolate Data

There are reasons to use interpolation; these include instances where you want to:

• replace dummy values in an X or Y channel with interpolated values. You may have entered dummy (placeholder) values to replace erroneous or noisy data.

• change the fiducial increment to a new starting fiducial and/or increment. For instance, in an airborne survey you may want to base all processing on a certain fiducial (time or distance) that is sampled at a certain interval.

Interpolation Methods

• Linear

• Minimum curvature

• Nearest

• Akima

• Prediction

Both 'Minimum curvature' and Akima interpolation methods define a second-order cubic spline to interpolate dummy values. 'Minimum curvature' produces the smoothest possible interpolation, but may create undesirable over-shoot in some areas, while Akima interpolation tends to be less smooth, but does not suffer from unreasonable over-shoot. 'Prediction' uses a maximum entropy prediction algorithm to predict the missing values such that they will have noise characteristics similar to the original data; when choosing this option, you can force the two ends of each line to be continuous and maintain periodicity. This is specifically needed for applying FFT filters. 'Linear' is a standard straight-line technique, and 'Nearest' assigns the value of the nearest non-dummy point.

To Interpolate Data in a Channel:

1. On the Database Tools > Channel Tools menu, select the Interpolate option (INTERP GX). The system displays the Interpolate dialog box.

2. Type an input and output channel, and select an interpolation method and the edge interpolation settings.

3. Click OK. The system places the interpolated data in the specified output channel.