1600 or 3600 measurements per 20m or 30m grid respectively, and are the recommended methodologies for archaeological surveys of this type (EH, 2008).
125. Data may be collected with a higher sample density where complex archaeological anomalies are encountered, to aid the detection and characterisation of small and ephemeral features. Data may be collected at up to 0.125m intervals along traverses spaced up to 0.25m apart, resulting in a maximum of 28800 readings per 30m grid, exceeding that recommended by English Heritage (English Heritage 2008) for characterisation surveys.
1.6.3.2 Post-Processing 126. The magnetic data collected during the detail survey are downloaded from the Bartington system for processing and analysis using both commercial and in-house software. This software allows for both the data and the images to be processed in order to enhance the results for analysis; however, it should be noted that minimal data processing is conducted so as not to distort the anomalies.
127. As the scanning data are not as closely distributed as with detailed survey, they are georeferenced using the GPS information and interpolated to highlight similar anomalies in adjacent transects. Directional trends may be removed before interpolation to produce more easily understood images.
128. Typical data and image processing steps may include: •
• •
Destripe – Applying a zero mean traverse in order to remove differences caused by directional effects inherent in the magnetometer;
Destagger – Shifting each traverse longitudinally by a number of readings. This corrects for operator errors and is used to enhance linear features;
Despike – Filtering isolated data points that exceed the mean by a specified amount to reduce the appearance of dominant anomalous readings (generally only used for earth resistance data);
•
Deslope – This is used to remove a linear trend from a dataset. It is most typically used to remove grid edge discontinuities that sometimes result from applying the zero mean traverse function; and
• Multiply – This function multiplies data by a positive or negative constant value. It is most commonly used to normalise a data set where differences in sensor height can result in varying background texture between grids.
Preliminary Environmental Information April 2014
East Anglia THREE Offshore Windfarm Appendix 25.1: Potential Archaeological Receptors Page 36
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