Geophysical data acquisition, processing and interpretation - Geophysics for the Mineral Exploration Geoscientist - page 74

by cells with their long axes horizontal and perpendicular
to the section being modelled ( Fig. 2.45b and d ).

The model parameters are adjusted interactively and the
model response recomputed. The number of iterations
required correlates closely with the expertise of the inter-
preter, the complexity of the model and the knowledge of
the subsurface geology. Obtaining a suitable match with
the observed data can be quite straightforward, provided
the computed response is easy to anticipate and that the
observed anomaly has a fairly simple form. Responses
from gravity and SP models are usually quite easy to
predict, but magnetic and electrical and electromagnetic
responses can be very difficult to predict, especially when
the model contains several sources. Forward modelling
also has an important role in survey design which is
discussed in Section 2.6.3.1 .
With inverse modelling, also known as inversion, the
iterative modelling process is automated; it is done by a
computer algorithm so that, from the interpreter

Two-and-a-half-dimensional model
A very useful variation on the 2D model which removes
the restriction of in nite strike length, and is easier to
de ne than the more complex 3D model, is a model with
constant cross-section extending over a finite strike length
( Fig. 2.47 ). This is known as a 2½ or 2.5D model. When
the source can have different strike extents on either side of
the modelled profile, or the strike or plunge of the body is
not perpendicular to the profile, this is sometimes called a
2.75D model. The 2.5D model gives the interpreter control
of the third (strike) dimension without the complexity of
defining and manipulating a full 3D model, so they are by
far the most used models for analysing all types of geo-
physical data in three dimensions. s view, it

appears as though the model parameters are obtained
directly from the set of field observations, with or without
some level of human intervention. Inverse modelling is a
far more dif cult proposition for the software engineer
than forward modelling, but for the interpreter the pro-
cess is normally simpler, in some cases apparently redu-
cing the interpretation procedure to a

'

Three-dimensional model
When the model of the subsurface can be varied in all three
directions, it is known as a three-dimensional model.
Shaped-based 3D models can be speci ed in a number of
ways, but usually as a network of interconnecting facets
( Fig. 2.44k and l ) . Cell-based models comprise a 3D distri-
bution of uniform cells ( Fig. 2.45a and c ).
Three-dimensional models can take considerably more
effort to define than 2D and 2.5D models and require
computer systems to view and manipulate the geometry
in three dimensions. The observed and modelled responses
are usually displayed as a series of profiles across sections
of interest. The simplest, and still very useful, form of 3D
model is the ellipsoid (and the sphere

.
However, inversion is deceptively simple. In most cases
the algorithm will produce a result but, unfortunately,
mathematical limitations of the inversion algorithm
and the phenomenon of geophysical non-uniqueness
(see Section 2.11.4 ) combine to produce many possible
models that will fit the data. The result from the inversion
is one of what may be an infinite number of possibilities,
and choosing the best one is often a major challenge in
itself for the interpreter. Forward modelling may be
slower and require more operator time and skill but,
consciously or not, the process gives the interpreter a
better understanding of the relationships between the data
and the subsurface, and a better appreciation of

' touch of a button '

a special case of the
ellipsoid). The model is very easy to manipulate and can
adequately represent a wide range of source shapes.

-

2.11.2 Forward and inverse modelling

the

There are two different modelling techniques for analysing
geophysical data: forward and inverse modelling. The main
differences between these is the level of human interaction
required to obtain a satisfactory match between the
observed and computed responses. Both can be applied
to cell- and shape-based models.
In forward modelling, the model parameters are
adjusted by the interpreter until a match is obtained. This
is an iterative process that requires the model and both the
observed and computed responses to be displayed graph-
ically so that the result can be assessed by the interpreter.

uncertainties in the resulting model.
There are situations when inverse modelling is essential:
when the link between the model and its geophysical
response is dif cult to anticipate; when the model com-
prises a great many parameters (as is usually the case with
cell-based models); and when a large volume of data needs
to be analysed quickly. Also, forward and inverse model-
ling can be used in combination. Inversion of a large
dataset will produce an initial model of the subsurface
which can be refined using either closely controlled inver-
sion or detailed forward modelling. Conversely, forward