Marine Geophysical Research

, Volume 39, Issue 1–2, pp 205–227 | Cite as

Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise, New Zealand

  • Jess I. T. HillmanEmail author
  • Geoffroy Lamarche
  • Arne Pallentin
  • Ingo A. Pecher
  • Andrew R. Gorman
  • Jens Schneider von Deimling
Original Research Paper


Using automated supervised segmentation of multibeam backscatter data to delineate seafloor substrates is a relatively novel technique. Low-frequency multibeam echosounders (MBES), such as the 12-kHz EM120, present particular difficulties since the signal can penetrate several metres into the seafloor, depending on substrate type. We present a case study illustrating how a non-targeted dataset may be used to derive information from multibeam backscatter data regarding distribution of substrate types. The results allow us to assess limitations associated with low frequency MBES where sub-bottom layering is present, and test the accuracy of automated supervised segmentation performed using SonarScope® software. This is done through comparison of predicted and observed substrate from backscatter facies-derived classes and substrate data, reinforced using quantitative statistical analysis based on a confusion matrix. We use sediment samples, video transects and sub-bottom profiles acquired on the Chatham Rise, east of New Zealand. Inferences on the substrate types are made using the Generic Seafloor Acoustic Backscatter (GSAB) model, and the extents of the backscatter classes are delineated by automated supervised segmentation. Correlating substrate data to backscatter classes revealed that backscatter amplitude may correspond to lithologies up to 4 m below the seafloor. Our results emphasise several issues related to substrate characterisation using backscatter classification, primarily because the GSAB model does not only relate to grain size and roughness properties of substrate, but also accounts for other parameters that influence backscatter. Better understanding these limitations allows us to derive first-order interpretations of sediment properties from automated supervised segmentation.


Bathymetry Backscatter Geomorphology Substrate classification Angular response 



We especially thank Jean-Marie Augustin at Ifremer for his help with data processing and providing access to SonarScope® software. We gratefully acknowledge the Helmholtz Centre for Ocean Research (GEOMAR), the National Institute of Water and Atmospheric Research (NIWA), the Oceans 2020 Survey, Land Information New Zealand (LINZ) and the United States Naval Research Laboratory (USNRL) for the provision of data presented in this paper. Thanks especially to the captains and crew of the R/V Sonne (Cruise SO226) for successful data-gathering expeditions. Thank you to Lisa Northcote at NIWA for conducting grain size and colour spectrophotometry analysis of sediment samples. Thanks to Hamish Bowman at the University of Otago for his help with data processing. We also thank Cord Papenberg and other members of the SO226 Scientific Party for their work in processing the seismic data presented in this paper. An academic license for IHS Kingdom® was used for geophysical data synthesis and analysis. Funding for the SO226 cruise was provided by the German Federal Ministry of Education and Research Grant 03G0226A issued to GEOMAR. This work was supported by the New Zealand Marsden Fund, Grant GNS1005.


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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Jess I. T. Hillman
    • 1
    Email author
  • Geoffroy Lamarche
    • 2
  • Arne Pallentin
    • 2
  • Ingo A. Pecher
    • 3
    • 4
  • Andrew R. Gorman
    • 1
  • Jens Schneider von Deimling
    • 5
  1. 1.Department of GeologyUniversity of OtagoDunedinNew Zealand
  2. 2.National Institute of Water and Atmospheric ResearchWellingtonNew Zealand
  3. 3.GNS ScienceLower HuttNew Zealand
  4. 4.School of EnvironmentUniversity of AucklandAucklandNew Zealand
  5. 5.Christian-Albrechts-University of KielKielGermany

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