At present, there are less than 100 icebergs for which comprehensive underwater measurements have been made. For iceberg load and risk analyses, there would be considerable benefit to generating large numbers of representative iceberg shapes that would have a smooth transition through the waterline and would not suffer from degradation in accuracy at the base of the keel. In a previous paper, a statistical characterization was developed for complete three-dimensional iceberg shapes, in which shape was represented in terms of spatially correlated deviations from an average iceberg radius. The present paper builds on this work through the analysis of additional data and through improvements to the iceberg generation scheme. Focus is on data from the 1982 and 1984 Hibernia Development Studies, Terra Nova 2003 Operations, and the DIGS 1985 experiment.

Keywords:
artificial intelligence, dimension, correlation function, hibernia 1982, iceberg, resolution, deviation, iceberg shape, mckenna, image understanding
Subjects:
Information Management and Systems
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Copyright 2006, The Society of Naval Architects and Marine Engineers
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