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3.4 Visualisation

Visualisation is used to display recorded data values in a way that can be easily understood by an interpreter. It is important to maintain the spatial characteristics of underlying measurements so that the morphology of archaeological features can be recognised.

Although geophysical data are recorded at individual positions ('point data'), they are affected to a larger or smaller degree by all features and soil in the vicinity. For sampling intervals up to about 1m, measurements can therefore be displayed as raster data, where each value is represented by a rectangular cell. It is filled with a colour or shade of grey from a palette, according to a predefined transfer function or look-up table. By altering the parameters of this transfer function (e.g. upper and lower clipping values, contrast) different features can be highlighted. Some software packages (e.g. Fortner Software's 'Transform') allow users to 'fiddle' (sic!) with this display by adjusting range and contrast according to the two-dimensional movement of a mouse pointer on the computer screen. Other display modes are also in use and line diagrams ('x/y plots') that show data as traces along the lines of survey are often requested (David 1995, 32).

To avoid artefacts often introduced by processing techniques (e.g. halos of high pass filters, see above) new methods of visualisation can be employed for the exploration of data. Cheetham (1996) introduced animation of geophysical results as an alternative to high-pass filtering. When displaying earth resistance data as a rapid sequence of images with narrow but overlapping clipping ranges, the human observer maintains an 'impression' of features that are visible in subsequent frames while a smoothly varying background is suppressed. In this way, weak anomalies on a gradually changing background can be identified. Such techniques do not lend themselves to paper-reproduction but have clear benefits and are already used on some Web pages (Marshall 1998).

Most geophysical techniques are used to create two-dimensional horizontal data maps, as these are similar to plan-views of an excavation and allow assessment of archaeological remains based on the shape of features. With GPR it has become possible to collect data in three dimensions by acquiring many parallel lines ('2D sections') to form a 'data cube'. Techniques for its processing and visualisation, however, have been developed only in the last few years (Conyers and Goodman 1997). In contrast to other geophysical techniques, GPR data only show interfaces and are not directly representative of a feature's properties. In this respect they are akin to first order derivatives and require sharp changes of dielectric permittivity in the ground (e.g. between soil and stone). However, Leckebusch and Peikert (2001) have shown that 3D migration techniques, originally developed for seismic data, can be used to derive a soil parameter called 'reflection strength' even for the interior of features and for gradual transitions between dielectric permittivities. This parameter hence represents the volume of buried features, and not just their horizontal interfaces. As such it lends itself to three-dimensional visualisation.

The easiest approach is to produce time-slices as 'horizontal' maps averaged over limited reflection time intervals (for example, Camerlynck et al. (1994) averaged over 5ns). They often show depth variations of features very well, especially if they are displayed sequentially with animation software. However, the time slices themselves are still two-dimensional and it is difficult to appreciate fully the stratigraphic and spatial relationships between different features, especially if they are tilting. Hence, data need to be visualised in three dimensions, using virtual reality models and stereo projections. A prerequisite is the calculation of 'reflection strength' for the whole ground volume, based on the measured reflections from horizontal interfaces (see above). Leckebusch (2001, 63) showed that subsequent conversion into iso-surfaces is required to compress the large datasets and allow for their real-time 3D exploration.

Magnetometer surveys are far less suited for 3D imaging, simply because at each position only one data value is measured, equivalent to recording a single layer. It is therefore possible to assume various feature arrangements with different magnetic parameters that would all produce the same measured values at the surface. Magnetic inversion has hence no unique solution (Blakely 1996, 217). However, if some prior information is used, for example the typical shape of a Roman ditch, or values of magnetic susceptibility of ploughsoil, subsoil and bedrock, a constrained inversion algorithm can be constructed that often produces very realistic information on the three-dimensional layout of buried archaeological features. Neubauer and Eder-Hinterleitner (1997b) used leaped annealing to calculate 3D models of Neolithic circular ring ditches, which were discovered with magnetometer surveys. These reconstructed models not only helped with the archaeological understanding of geophysical anomalies but also revealed the plough damage to these monuments more clearly than the original data. Dittrich and Koppelt (1997) used genetic algorithms and Herwanger et al. (2000) iterative least-square inversion with considerable prior assumptions to achieve similar results.

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