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Improving multi-beam echo sounder depth measurements
Snellen, M.; van den Ameele, J.J.P.; Biersteker, R.; Simons, D.G. (2006). Improving multi-beam echo sounder depth measurements, in: Evolutions in hydrography, 6th - 9th November 2006, Provincial House Antwerp, Belgium: Proceedings of the 15th International Congress of the International Federation of Hydrographic Societies. Special Publication (Hydrographic Society), 55: pp. 97-101
In: (2006). Evolutions in hydrography, 6th - 9th November 2006, Provincial House Antwerp, Belgium: Proceedings of the 15th International Congress of the International Federation of Hydrographic Societies. Special Publication of the Hydrographic Society, 55. International Federation of Hydrographic Society: London. 234 + cd-rom pp., more
In: Special Publication (Hydrographic Society). Hydrographic Society: London. ISSN 0309-8303, more

Available in  Authors 
Document type: Conference paper

Keywords
    Bathymetric data; Depth measurement; Echosounding; Multibeam sonar

Authors  Top 
  • Snellen, M.
  • van den Ameele, J.J.P.
  • Biersteker, R.
  • Simons, D.G.

Abstract
    An important research question is how to adequately correct multi-beam echo sounder (MBES) bathymetric data for refraction effects. This is especially relevant for survey areas, like the Maasgeul area off the Dutch coast, where the water column properties and thus the prevailing sound speed profile (SSP), are highly dynamic. Expensive, and therefore not widely applied, towed systems are available for performing SSP measurements continuously. Other options provide sparse SSP measurements only. Consequently, in practice, correcting for refraction is hampered due to insufficient knowledge of the prevailing SSP at the time of the MBES measurement. We present a new approach to correct for refraction. This approach requires and fully exploits the possibility of the MBES to survey adjacent swathes with overlap. Due to variations in the prevailing SSP and sparse SSP measurements, depths determined at overlapping points will in general differ. Here, a Monte Carlo search is used to find SSPs that minimize these depth differences. To this extent the SSP is parameterized according to a set of basis functions determined from historical SSP data from the survey area.

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