Geoscience Reference
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drainage networks can also be a problem (see Section
3.11.4 ) , although these are easy to recognise by their char-
acteristic dendritic form. Glacial deposits can be a signifi- -
cant source of magnetism owing to the presence of
magnetic materials plucked from the bedrock and re-
deposited by glacial processes. Magnetic responses due to
glacial features are described by Parker Gay ( 2004 ). Both
gravity and magnetic responses can be caused by lateral
changes in the thickness of the glacial deposits, which may
be due to channels; but particularly important are the
effects of glacial landforms whose relief affects the distance
between sensor and source, such as drumlins, moraines
and younger river channels that may have removed large
sections of laterally extensive deposits. Effects of the near-
surface diminish in downhole work.
Section 3.6.4 ) . Features oriented parallel to the survey lines
should be carefully considered to ensure they are not
survey artefacts.
The interpreter should always be aware of the possible
presence of processing artefacts in the transformed datasets
and avoid erroneously interpreting them as genuine geo-
logical features. The possibility of artefacts being produced
by the reduction-to-pole and pseudogravity transforms is a
particular problem for magnetic data (see Sections 3.7.2
and 3.7.2.2 ) .
3.10.4 Estimating depth-to-source
The large contribution that the upper region of a source
( Fig. 3.65 ) makes to the gravity and magnetic responses
above the ground surface means that the data are sensitive
to the depth to the top of the source. Depth-to-source is
most accurately determined by modelling the anomaly (see
Section 2.11 ). Often though, it is necessary to make an
estimate of the depth of the source of an anomaly quickly
and easily, or depth estimates of a large number of anom-
alies in a dataset may be required. Depth-to-source estima-
tion is the simplest form of inverse modelling (see Section
2.11.2 ) .
Manual depth-to-source techniques, now largely obso-
lete, involve identifying certain characteristics (zero cross-
over points, gradients, distance between inflection points
etc.) of the central or principal profile of the anomaly
which, ideally, passes through its maximum and minimum
peaks, and usually trends perpendicular to its gradients.
Details for various source geometries are given by Am
( 1972 ), Atchuta Rao and Ram Babu ( 1984 ) , and Blakely
( 1995 ) provides a compilation of the various methods; see
also Salem et al.( 2007 ) . Modelling techniques that work
with all the data points to
3.10.3.3 Topographic effects
Topographic effects can be expected in both gravity and
magnetic data acquired in rugged terrains (see Section
2.4.1.1 ) . Variations in terrain clearance are common in
airborne data. Gravity data are highly susceptible to
errors in the terrain correction (see Section 3.4.5.3 ) ,
and particularly for topography local to the station for
which accurate de nition of features is essential. Care
is required where responses coincide with
'
changes in the topography, such as a scarp; they may
be poorly described by the DTM data. In high-resolution
surveys, even sand dunes may affect the data, owing to
their topographic effect in gravity and their possible
magnetic mineral content in magnetics. Topographic
effects can be identified by observing correlations
between gravity and magnetic images and topographic
data. There is no practical way of removing topographic
effects other than to include the topography in computer
models and account for its effect in the interpretation of
the survey data.
'
step-like
nd a
'
best-
t
'
to the whole
pro
le provide more reliable results.
3.10.3.4 Survey and data processing effects
An irregular distribution of survey stations can produce
distortions in the observed anomalies (see Fig. 2.18 ) and in
the various transforms of the gridded data, particularly
where the stations are sparse. This can be easily con rmed
by overlaying the station locations on the data and its
various transforms. In the case of airborne surveys, the
survey line direction and spacing has an important effect
on the dataset because, as shown in Fig. 3.24 , inter-line
variations can be difficult
3.10.4.1 Euler deconvolution
Euler (pronounced
) deconvolution (Reid et al., 1990 ;
Zhang et al., 2000 ) is a commonly used semi-automated
depth-to-source method useful for quickly analysing a
large number of responses in a dataset. The method is
based on anomaly gradients for selected source geometry
and is sequentially applied to all the points along the
anomaly pro le.
Euler
'
oiler
'
'
s equation represents the strength (f) of the poten-
tial field at a point (x, y, z) in space, due to a source located
to remove completely (see
 
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