Geophysical data acquisition, processing and interpretation - Geophysics for the Mineral Exploration Geoscientist - page 54

The human eye can only differentiate about 30 shades of
grey, so if more are used in the display the variation
appears continuous. Grey-scale displays are useful for
some types of data, but generally more detail is revealed
when colour is used.
The most common and probably most effective LUTs
comprise a rainbow-like spectrum of colours, i.e. the order
of the colours in the visible spectrum, although not all are
necessarily included. Images created with three different
spectrum-based LUTs are shown in Figs. 2.32b , c and d .
Lower values in the LUT are assigned to purples and dark
blues; intermediate values assigned to shades of lighter
blue, green and yellow; and higher values assigned to
oranges, reds and finally white. This kind of colour map
associates cold colours such as blues with low data values,
and warm colours such as oranges and reds with high data
values. When displaying data where polarity are important,
such as seismic data, a colour map comprising two colours
separated by a thin central band of white is very effective
(see Fig. 6.12 ) .
There is no firm basis for a particular colour scheme
being superior to another; the choice really depends on the
nature of the data being displayed, the nature of the inter-
pretation and the interpreter

values assigned to each class interval ( Fig. 2.33 ) . The vari-
ations in the data are now no longer represented by their
true values; instead they are represented by the class inter-
val in which they fall, 0 representing the lowest value class
interval, 255 the highest. The 256 data classes are mapped
to the 256 colours comprising the colour map using a
stretch function, i.e. the available colours are

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stretched

across the data histogram.
A linear colour stretch across the full range of 256 data
classes maps each class to its corresponding colour value in
the LUT, with the lowest data class (0) mapping to the
lowest colour value (0) through to the highest data value
(255) mapping to the highest colour value. In other words,
there is a linear relationship between the data classes and
the colour values. When the data histogram shows that the
majority of the data fall within a comparatively small
number of classes, which is often the case for geophysical
data, a large proportion of the data are assigned to just a
few of the display colours. The result is an image which is
composed predominantly of just a few colours. In this case,
a few extreme data values, the outliers, are exerting a
disproportionate in uence on the image producing a dis-
play which prevents variations across the full range of the
data from being recognised. In other words, the data values
do not make full use of the available colour values, so the
display is said to lack contrast. This is not a problem when
anomalous readings are the principal interest, as is the case
for anomaly detection, but for more general interpretations
of the whole data grid, e.g. for mapping, a different stretch
function is required.
A simple solution for improving the contrast is to
redefine the linear stretch so that it only extends over
that part of the data range where most values occur.
Figure 2.33a shows a linear function chosen to span the
main range of variation in the data. In this case, an
arbitrarily chosen data value (G) maps to colour value
209, a mid-range red. The few low-valued data points
below the lower limit of the linear stretch all map to the
lowest colour value (0) and, similarly, high-valued points
beyond the upper limit all map to the highest colour
value (255).
It is useful to illustrate the effects of the stretch using a
display histogram, which is a frequency histogram of the
colour values. It can be plotted next to the data histogram.
Both have 256 class intervals; in the data histogram they
are populated by the data values and in the display histo-
gram by the colour values. For the case of the localised
linear stretch shown in Fig. 2.33a , the display histogram is

s personal preference (and
colour vision). In general, schemes producing greater vari-
ation in colour allow more subtle detail to be seen and are
preferable for geological mapping. On the other hand,
simpler schemes can be effective when the primarily aim
is to identify anomalous responses which may constitute
targets. It is worth noting that the human visual system is
not equally adept at seeing variations in different colours
(Welland et al., 2006 ). It is worst at recognising different
shades of red and blue, so LUTs comprising predominantly
these colours should be avoided.

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Colour stretch
Once the LUT has been selected, the 256 colours are then
assigned or mapped to variations in the data. An important
tool for controlling this is a frequency histogram. The data
are first assigned to one of 256 class intervals. Data values
falling between the maximum and minimum values of the
interval are assigned to the interval. Each interval has the
same width, and this width is chosen such that the 256
intervals span the entire range of the data, or at least most
of the range. To reduce the in uence of outlier data values
the class interval width may be scaled so as to extend across
a fraction of the entire data range, 95% of the range for
example. The data histogram shows the number of data