Gravity and magnetic methods - Geophysics for the Mineral Exploration Geoscientist - page 167

focusing the process on sources at different depths. How-
ever, the larger the window the more likely that more than
one source will in uence the data within it, which will
create spurious results. The quality of the data, i.e. sam-
pling interval and noise, affects the result.
Euler deconvolution produces many solutions for an
anomaly, most of which are spurious. Various techniques
are used to identify the best solutions. These include
assessing how well the solutions fit the data in each
window; assessing the clustering of solutions from different
windows; and comparing results for various values of N
and only providing the solutions for the best-fit value.
Various improvements have been proposed such as solving
for several sources simultaneously (to address anomaly
overlap), extracting additional information about source
characteristics, and more sophisticated means of choosing
the best solution from the large number produced. See, for
example, Mushayandebvu et al.( 2001 ).
Euler solutions are usefully analysed in terms of depths
and horizontal positions. Three-dimensional results are
presented in map form with different source depths and
geometries (different N) distinguished using variations in
colour and/or symbol type ( Fig. 3.71 ). The edges of source
bodies can be mapped in this way. Two-dimensional data
are presented as points on a cross-section, with different N
represented by different symbols.
Euler deconvolution can be applied to gradient data, in
which case second-order derivatives are required. The
advantage of using gradients is that they are more localised
to the source and have greater immunity to neighbouring
sources resulting in better spatial resolution in the Euler
solutions. A disadvantage is that gradients have poorer
signal-to-noise ratios than normal field data, especially
higher-order derivatives, so the quality of the results may
be signi

field in the survey area be known as they are fundamental
in determining the response (see Section 3.2.4 ) .
Modelling a targeted anomaly usually requires that it be
isolated from the background response, achieved by
removing the background as a regional field (see Section
2.9.2 ) . The broader gravity responses almost always con-
tain superimposed responses from neighbouring sources,
and separating the target anomaly from these can be diffi- -
cult. This is less of a problem with magnetic data. Model-
ling the background variation with the target anomaly can
often provide a satisfactory result (see Section 2.11.3.1 ).
When modelling Bouguer gravity anomalies, it can be
confusing to reconcile subsurface depths and depths of
model sources which occur below the surface, but above
the level of the reduction datum. In this case it may be
easier to include the topography in the model and work
with free-air gravity anomalies. The Bouguer slab and
terrain effects (see Section 3.4.5 ) are accounted for by
including the topography in the model, and depths of
features in the model then correspond to depths below
the topographic surface. Also, the model can then directly
account for possible lateral and vertical changes in density
above the datum level, and so avoids the problem of
estimating an average single density for the Bouguer cor-
rection. The modelling of magnetic data should also
include topography.

3.10.6 Modelling pitfalls

Modelling of any geophysical data is only as good as the
assumptions made when simplifying the complex real-
world variations in physical properties into a model that
is defined by a manageable number of parameters. We
reiterate the points made in Section 2.11.3 on the import-
ance of accounting for noise levels, choosing an appropri-
ate type of model and creating the simplest possible model
that matches the observations so as not to imply infor-
mation that is not supported by the data. In particular,
when modelling potential field data, account must also be
taken of ambiguity: the unavoidable fact that without con-
straints provided by other sources of data, the effects of
equivalence and non-uniqueness mean that an in nite
number of density or magnetism distributions in the sub-
surface can reproduce the observed anomalies.

cantly degraded.

3.10.5 Modelling source geometry

The principles of modelling and the types of models avail-
able for the quantitative analysis of geophysical data are
described in Section 2.11 ). Here we discuss some aspects
speci c to modelling potential field data. A practical
description of how to go about modelling gravity and
magnetic data is provided by Leaman ( 1994 ).
A demonstration of modelling the magnetic anomaly asso-
ciated with the Wallaby Au deposit is given in Section
3.11.2 . Note that when working with magnetic data it is
essential that the strength and orientation of the inducing

3.10.6.1 Equivalence
Recall that only relative changes in gravity and magnetic
strength are of significance in mineral exploration