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interpretation. By using both the data in its basic form
and its various transformations, such as derivatives,
and by making use of different kinds of display, for
example grey-scale, pseudocolour etc., more information
can be displayed, making for a much more reliable
interpretation.
interpreter, is simply not possible; instead we describe a set
of somewhat idealised guidelines. We use the familiar
exercise of making a geological map to provide context.
All available relevant factual information from the
survey area needs to be compiled prior to beginning the
interpretation of the geophysical data. Geological data are
fundamental to the process and include outcrop informa-
tion, orientation measurements (bedding, fold axes etc.)
and drillhole intersections. The interpreter needs to con-
sider the possibility of inconsistent lithological identifica-
tions if the work of several individuals is combined, and be
mindful that a geological map is itself an interpretation.
Ideally, a geological fact (outcrop) map should be inte-
grated with the geophysical data being interpreted in a
computer-based Geographical Information System (GIS)
environment. Examples of this kind of data integration are
provided by Haren et al.( 1997 ) .
Petrophysical measurements, if available, are useful for
predicting the responses of the local geology. In the
absence of data from the survey area, the generalities about
rock physical properties given in our descriptions of each
geophysical method can be used, albeit with caution. Par-
ticularly important is the identi cation of the principal
physical-property contrasts in the area, since these will
correspond with the most prominent features in the geo-
physical data. Variability within a set of petrophysical
measurements can have important implications. For
example, a rock unit with a large range in a physical
property will exhibit greater textural variation in its geo-
physical response. Also, a bimodal distribution may sug-
gest that two
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Although various forms of the data may emphasise par-
ticular characteristics, other important characteristics may
be obscured; so the interpreter should continually make
reference to the most fundamental formof the geophysical
data, e.g. the magnetic or gravity field strength, the calcu-
lated electrical conductivity/resistivity, etc., described in a
simple form such as a colour-draped display ( Fig. 2.36 ) .
As demonstrated by Figs. 2.41 and 2.42 , the human vision
system is not infallible. Many of the potential traps can be
addressed in two ways. For reasons not fully understood,
the same interpreters will make different interpretations
of the same data if presented in a different orientation
(Sivarajah et al., 2013 ) . Why this is the case is the subject
of current research, but the problem is easily addressed
by rotating the data during interpretation.
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The use of several illumination directions in shaded
relief is important because of its inherent directional
bias, which can lead to incorrect or incomplete identifi- -
cation of linear features.
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extrapolation or interpolation. This can be overcome by
checking the interpretation whilst viewing a small
window or subarea of the dataset.
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Many optical illusions are a form of incorrect
mental
The need for the interpreter to continually evaluate the
geological credibility of features interpreted from the data
cannot be over-emphasised. The integration of other types
of geophysical data, and geological, geochemical and topo-
graphic data, is essential for developing an accurate inter-
pretation of the data. The interpreter is more likely to be
fooled if they treat their interpretation as
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of the same lithological unit can be
mapped, even though they appear identical when exam-
ined in the field. It is also important to bear in mind that
the petrophysical sampling may not be representative. The
petrophysical data may be biased towards those parts of a
unit that are resistant to weathering, and no data at all may
be available from units that weather easily.
An interpretation procedure effective for most situations
involves, firstly, developing a framework by identifying
linear and curvilinear features and, secondly, classifying
regions based on their textural and tonal characteristics,
and the wavelength or width of the measured responses.
Normally the procedure is iterative, i.e. the results from
any one stage of the interpretation are re-evaluated in
terms of the evolving interpretation. It may be necessary
to model some of the observed responses (see Section
2.11.2 ) in order to help understand both the sources of
the responses and the surrounding geology.
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types
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factual
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without
making the essential credibility checks.
2.10.2 Geophysical image to geological map
Creating a pseudo-geological map of a large area from
geophysical data can be a complex and time-consuming
task, so often only areas of particular exploration interest
are mapped in detail. Geophysical datasets are as variable
as the geological environments that give rise to them, and
different datasets must be considered in different ways.
De ning a set of rigorous interpretation rules applicable
to all situations, and satisfying the requirements of every
 
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