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Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization

Abstract

This study applies three classification methods exploiting the angular dependence of acoustic seafloor backscatter along with high resolution sub-bottom profiling for seafloor sediment characterization in the Eckernförde Bay, Baltic Sea Germany. This area is well suited for acoustic backscatter studies due to its shallowness, its smooth bathymetry and the presence of a wide range of sediment types. Backscatter data were acquired using a Seabeam1180 (180 kHz) multibeam echosounder and sub-bottom profiler data were recorded using a SES-2000 parametric sonar transmitting 6 and 12 kHz. The high density of seafloor soundings allowed extracting backscatter layers for five beam angles over a large part of the surveyed area. A Bayesian probability method was employed for sediment classification based on the backscatter variability at a single incidence angle, whereas Maximum Likelihood Classification (MLC) and Principal Components Analysis (PCA) were applied to the multi-angle layers. The Bayesian approach was used for identifying the optimum number of acoustic classes because cluster validation is carried out prior to class assignment and class outputs are ordinal categorical values. The method is based on the principle that backscatter values from a single incidence angle express a normal distribution for a particular sediment type. The resulting Bayesian classes were well correlated to median grain sizes and the percentage of coarse material. The MLC method uses angular response information from five layers of training areas extracted from the Bayesian classification map. The subsequent PCA analysis is based on the transformation of these five layers into two principal components that comprise most of the data variability. These principal components were clustered in five classes after running an external cluster validation test. In general both methods MLC and PCA, separated the various sediment types effectively, showing good agreement (kappa >0.7) with the Bayesian approach which also correlates well with ground truth data (r2 > 0.7). In addition, sub-bottom data were used in conjunction with the Bayesian classification results to characterize acoustic classes with respect to their geological and stratigraphic interpretation. The joined interpretation of seafloor and sub-seafloor data sets proved to be an efficient approach for a better understanding of seafloor backscatter patchiness and to discriminate acoustically similar classes in different geological/bathymetric settings.

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Acknowledgements

We would like to specifically thank Sebastian Krastel (Institute of Geosciences, Christian-Albrechts-Universität, Kiel) and Christian Berndt (GEOMAR) for providing access to the MBES dataset collected in 2012. The SBP survey was supported by an Innomar Student Project which allowed us using the parametric SES-2000 sub-bottom profiler system with help by Peter Hümbs from Innomar. We would also like to thank, RV Littorina captain and crew, and Wärtsilä ELAC Nautik for providing a special pole for the SBP survey. Two anonymous reviewers are thanked as well as Dr. Geoffroy Lamarche for valuable comments and recommendations. We also thank Dr. Karin Meissner (Senckenberg) for providing the identification of macro-benthic organisms. Finally thanks go to Edna Hütten and Thomas J. Browning for providing valuable grammar and syntax corrections. This is publication 28 of the DeepSea Monitoring Group at GEOMAR.

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Correspondence to Evangelos Alevizos.

Appendix

Appendix

See Tables 4 and 5.

Table 4 Population statistic features and frequency distributions (x axis: backscatter (dB), y axis: frequency) of soundings from five sediment sample locations (5 × 5 m patches)
Table 5 Percentages and grain size statistics of the coarse and fine grain fractions, presence of shells from sieved data measurements and classifications results

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Alevizos, E., Snellen, M., Simons, D. et al. Multi-angle backscatter classification and sub-bottom profiling for improved seafloor characterization. Mar Geophys Res 39, 289–306 (2018). https://doi.org/10.1007/s11001-017-9325-4

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Keywords

  • Acoustic backscatter
  • Angular response analysis
  • Bayesian statistics
  • Sediment classification
  • Sub-bottom