Standard processing techniques, like high- and low-pass filtering, or the reduction-to-the-pole of magnetometer data (Blakely 1996, 330), use the original measurements to calculate new data maps that can be interpreted more easily as archaeological features. Such interpretation therefore requires a profound understanding of the geophysical nature of the measurements. For example, in magnetometer data a positive peak with an adjacent negative trough should not be interpreted as two separate features (e.g. 'rampart and ditch') since such geophysical anomalies are normally caused by just one single entity. To simplify the archaeological interpretation 'complex attributes' can be calculated. Tsokas and Hansen (2000) showed how magnetometer data are used to calculate new parameters (e.g. 'depth to interface', magnetic susceptibility) that simplify interpretation by including the geophysical knowledge in an algorithm and provide a more user-friendly output. When taking this idea further, algorithms for 'automated interpretations' can be devised. Sheen (1998) showed how a hybrid artificial neural network is used to locate significant features in magnetometer data and estimated their depth and width. Comments about the dangers of a 'black box-approach' apply even more to such methods. The best solution might be for a skilled operator to compare measured data and computed results so that an informed decision about their archaeological interpretation can be made. If the only task then remaining is the final labelling of automatically delineated anomalies, interpreters have been freed of some rather mechanistic chores — a most desirable improvement.
For the calculation of 'complex attributes', more than one dataset can be used, for example earth resistance and magnetometer data. Such a combination, sometimes referred to as 'data fusion', derives new information from the response of archaeological features to different survey techniques and is similar to multispectral image classification (see above). For example, if magnetometer data are high and earth resistance measurements low, the causative feature might be a ditch. If, however, the magnetic anomaly is very high and earth resistance is high, it might be a kiln. In this way a classified attribute would help considerably with the archaeological interpretation. A major problem with this approach is the variation in spatial characteristics (Schmidt 2001b) of different geophysical techniques (e.g. peak and trough of magnetic anomalies, see above, and single peaks in earth resistance data). To normalise different anomalies, considerable processing is required and further research in this area is needed (Piro et al. 2000).
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Last updated: Tue Jan 27 2004