Glossary

Image normalisation

Normalisation is the assignment of the range of image data values to a range of colours. This allows the creation of a false-colour image, in which (for example) the most conductive features are black and the most resistive are white, with a range of brown/tan/yellow tones defining the middle values of the spectrum.

There are two types of normalisation applied to image data:

Static normalisation: the colour mapping applied to the image data values is determined using the range of values encountered over the entire logged section. Static image normalization thus illustrates the gross variation in the parameter being measured through the succession.

Dynamic normalisation: the colour mapping applied at any point in the image is determined using the range of values encountered in a small data interval immediately surrounding that point (e.g. 2ft). This interval is moved stepwise through the logged succession to create a dynamically changing colour map; because the data range is likely to be smaller than that used in static normalisation, dynamic normalisation generally gives a greater level of detail to the image. Dynamic normalisation using small data windows therefore enhances small scale data variations, allowing the interpreter to visualize sedimentary and structural fabrics that are not associated with large variations. However, the same colour seen at different depths in the dynamically normalised false coloured image may reflect very different data values; this is the principal difference between dymanic and static normalization.