Interpolate Data
There are reasons to use interpolation; these include instances where you want to:
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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.
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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.
The Spreadsheet and Profile windows automatically compensate for the channels that are sampled differently.
Interpolation Methods
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Linear
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Minimum curvature
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Nearest
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Akima
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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:
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On the Database Tools > Channel Tools menu, select the Interpolate option (INTERP GX). The system displays the Interpolate dialog box.
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Type an input and output channel, and select an interpolation method and the edge interpolation settings.
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Click OK. The system places the interpolated data in the specified output channel.
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