Interpolate Data

Interpolation is useful in several scenarios, such as:

• Replacing dummy values in an X or Y channel with interpolated data. Dummy (placeholder) values may be used to mask erroneous or noisy measurements.

• Adjusting fiducial increments to a new starting point and/or interval (increment). For example, in airborne surveys, you might want to base processing on a specific fiducial (time or distance) sampled at a consistent 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 cause undesirable overshoot in some areas, whereas Akima interpolation tends to be less smooth but does not suffer from excessive overshoot.

Prediction applies a maximum entropy prediction algorithm to estimate missing values with similar noise characteristics. Useful for FFT filtering. You can enforce continuity and periodicity at line ends.

Linear is a standard straight-line interpolation between points. Nearest assigns the value of the nearest non-dummy point.

How to Interpolate Data in a Channel

1. Go to Database Tools > Channel Tools, and select Interpolate (INTERP GX).

2. In the Interpolate dialog box:

   • Specify the input and output channels

   • Choose an interpolation method.

   • Set the edge interpolation options.

3. Click OK. The interpolated data will be placed in the specified output channel.