Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research Paper
  • Published:
Marine Geophysical Research Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Altman DG, Bland JM (1994) Statistics notes: diagnostic tests 2: predictive values. BMJ 309:102–102. doi:10.1136/bmj.309.6947.102

    Article  Google Scholar 

  • Anderson JT (eds) (2007) Acoustic seabed classification of marine physical and biological landscapes. International Council for the Exploration of the Sea, Copenhagen

    Google Scholar 

  • Augustin JM, Lurton X (2005) Image amplitude calibration and processing for seafloor mapping sonars. In: Oceans 2005: Europe, vol 1. IEEE Explore, Washington, pp 698–701

    Chapter  Google Scholar 

  • Barnes PM (1992) Mid-bathyal current scours and sediment drifts adjacent to the Hikurangi deep-sea turbidite channel, eastern New Zealand: evidence from echo character mapping. Mar Geol 106:169–187

    Article  Google Scholar 

  • Bialas J, Klaucke I, MÓ§geltÓ§nder J (eds) (2013) RV SONNE Fahrtbericht/Cruise Report SO226 - CHRIMP Chatham Rise Methane Pockmarks, 07.01. – 06.02.2013/Auckland–Lyttelton, 07.02. – 01.03.2013/Lyttelton–Wellington. GEOMAR Report, N. Ser. 007. GEOMAR Helmholtz-Zentrum für Ozeanforschung, Kiel

    Google Scholar 

  • Blott SJ, Pye K (2001) GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf Process Landforms 26:1237–1248

    Article  Google Scholar 

  • Bowden D (2011) Benthic invertebrate samples and data from the Ocean Survey 20/20 voyages to the Chatham Rise and Challenger Plateau, 2007. National Institute of Water and Atmospheric Research (NIWA), Wellington

    Google Scholar 

  • Brown CJ, Blondel P (2009) Developments in the application of multibeam sonar backscatter for seafloor habitat mapping. Appl Acoust 70:1242–1247 doi:10.1016/j.apacoust.2008.08.004

    Article  Google Scholar 

  • Coffin RB, Boyd TJ, Rose PS et al (2013) Geochemical cruise report: SO226/2 RV Sonne Chatham Rise expedition. US Naval Research Laboratory, Washington

    Google Scholar 

  • Collier JS, Brown CJ (2005) Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Mar Geol 214:431–449

    Article  Google Scholar 

  • Collins WT, Preston JM, Christney AC, Rathwell GJ (2002) Production processing of multibeam backscatter data for sediment characterisation. In: Caris 2002. Quester Tangent Corporation, Norfolk

    Google Scholar 

  • Cook RA, Wood RA, Campbell HJ (1989) The Chatham Rise: an exploration frontier. In: 1989 petroleum conference. Ministry of Economic Development, New Zealand Petroleum and Minerals, New Zealand, pp 35–41

    Google Scholar 

  • Cullen DJ (1987) The submarine phosphate resource on central Chatham Rise. National Institute of Water and Atmospheric Research (NIWA), Wellington

    Google Scholar 

  • Dartnell P, Gardner JV (2004) Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogramm Eng Remote Sensing 70:1081–1091

    Article  Google Scholar 

  • de Moustier C, Alexandrou D (1991) Angular dependence of 12-kHz seafloor acoustic backscatter. J Acoust Soc Am 90:522–531

    Article  Google Scholar 

  • Diesing M, Green SL, Stephens D et al (2014) Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Cont Shelf Res 84:107–119. doi:10.1016/j.csr.2014.05.004

    Article  Google Scholar 

  • Dormann CF, McPherson JM, Araújo MB et al (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography (Cop) 30:609–628

    Article  Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27:861–874. doi:10.1016/j.patrec.2005.10.010

    Article  Google Scholar 

  • Ferrini VL, Flood RD (2006) The effects of fine-scale surface roughness and grain size on 300 kHz backscatter intensity in sandy marine sedimentary environments. Mar Geol 228:153–172

    Article  Google Scholar 

  • Fonseca L, Mayer L (2007) Remote estimation of surficial seafloor properties through the application angular range analysis to multibeam sonar data. Mar Geophys Res 28:119–126. doi:10.1007/s11001-007-9019-4

    Article  Google Scholar 

  • Fonseca L, Brown C, Calder B et al (2009) Angular range analysis of acoustic themes from Stanton Banks Ireland: a link between visual interpretation and multibeam echosounder angular signatures. Appl Acoust 70:1298–1304 doi:10.1016/j.apacoust.2008.09.008

    Article  Google Scholar 

  • Friedl MA, Woodcock C, Gopal S et al (2000) A note on procedures used for accuracy assessment in land cover maps derived from AVHRR data. Int J Remote Sens 21:1073–1077. doi:10.1080/014311600210434

    Article  Google Scholar 

  • Goff JA, Kraft BJ, Mayer LA et al (2004) Seabed characterization on the New Jersey middle and outer shelf: correlatability and spatial variability of seafloor sediment properties. Mar Geol 209:147–172 doi:10.1016/j.margeo.2004.05.030

    Article  Google Scholar 

  • Guillon L, Lurton X (2001) Backscattering from buried sediment layers: the equivalent input backscattering strength model. J Acoust Soc Am 109:122–132

    Article  Google Scholar 

  • Hammerstad E (2000) EM techinical note: backscattering and seabed image reflectivity 1–5

  • Hershey JR, Olsen PA (2007) Approximating the Kullback Liebler divergence between Gaussian mixture models. Int Conf Acoust Speech Signal Process 4:317–320

    Google Scholar 

  • Hughes Clarke JE, Mayer LA, Wells DE (1996) Shallow-water imaging multibeam sonars: a new tool for investigating seafloor processes in the coastal zone and on the continental shelf. Mar Geophys Res 18:607–629

    Article  Google Scholar 

  • Jackson DR, Briggs KB (1992) High-frequency bottom backscattering: roughness versus sediment volume scattering. J Acoust Soc Am 92:962–977

    Article  Google Scholar 

  • Jones EJW (1999) Marine geophysics. Wiley, Chichester

    Google Scholar 

  • Kagesten G, Bengt L (2008) Geological seafloor mapping with backscatter data from a multibeam echosounder. Thesis, Gothenburg University

  • Karoui I, Fablet R, Boucher JM, Augustin JM (2009) Seabed segmentation using optimized statistics of sonar textures. IEEE Trans Geosci Remote Sens 47:1621–1631. doi:10.1109/Tgrs.2008.2006362

    Article  Google Scholar 

  • Karoui I, Fablet R, Boucher JM, Augustin JM (2010) Variational region-based segmentation using multiple texture statistics. IEEE Trans Image Process 19:3146–3156. doi:10.1109/Tip.2010.2071290

    Article  Google Scholar 

  • Kenny AJ, Cato I, Desprez M et al (2003) An overview of seabed-mapping technologies in the context of marine habitat classification. ICES J Mar Sci 60:411–418

    Article  Google Scholar 

  • Lamarche G, Lurton X, Verdier AL, Augustin JM (2011) Quantitative characterisation of seafloor substrate and bedforms using advanced processing of multibeam backscatter-application to Cook Strait, New Zealand. Cont Shelf Res 31:93–109. doi:10.1016/j.csr.2010.06.001

    Article  Google Scholar 

  • Lamarche G, Orpin AR, Mitchell J (2015) Chap. 5: benthic habitat mapping. In: Clark MR, Consalvey M (eds) Biological sampling in the deep sea: an illustrated manual of tools and techniques. Wiley-Blackwell, New York, pp 80–102

    Google Scholar 

  • Le Gonidec Y, Lamarche G, Wright IC (2003) Inhomogeneous substrate analysis using EM300 backscatter imagery. Mar Geophys Res 24:311–327. doi:10.1007/s11001-004-1945-9

    Article  Google Scholar 

  • Lucieer VL (2007) The application of automated segmentation methods and fragmentation statistics to characterise rocky reef habitat. J Spat Sci 52:81–91. doi:10.1080/14498596.2007.9635104

    Article  Google Scholar 

  • Lucieer V, Lamarche G (2011) Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Cont Shelf Res 31:1236–1247. doi:10.1016/j.csr.2011.04.016

    Article  Google Scholar 

  • Lurton X (2010) An introduction to underwater acoustics: principles and applications. Springer, New York

    Book  Google Scholar 

  • Lurton X, Lamarche G (eds) (2015) Backscatter measurements by seafloor-mapping sonars. Guidelines and recommendations. GeoHab Report 200pp. http://geohab.org/publications/

  • Lyons AP, Anderson AL, Dwan FS (1994) Acoustic scattering from the seafloor: modelling and data comparison. J Acoust Soc Am 95:2441–2451

    Article  Google Scholar 

  • Marani M, Argnani A, Roveri M, Trincardi F (1993) Sediment drifts and erosional surfaces in the central Mediterranean: seismic evidence of bottom-current activity. Sediment Geol 82:207–220. doi:10.1016/0037-0738(93)90122-L

    Article  Google Scholar 

  • Marsh I, Brown C (2009) Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV). Appl Acoust 70:1269–1276. doi:10.1016/j.apacoust.2008.07.012

    Article  Google Scholar 

  • Masson D, Howe J, Stoker M (2002) Bottom-current sediment waves, sediment drifts and contourites in the northern Rockall Trough. Mar Geol 192:215–237. doi:10.1016/S0025-3227(02)00556-X

    Article  Google Scholar 

  • Matthews BW (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta Protein Struct 405:442–451. doi:10.1016/0005-2795(75)90109-9

    Article  Google Scholar 

  • McDougall JC (1982) Bounty sediments. New Zealand Oceanographic Institute Chart, Oceanic Series 1:1000000

    Google Scholar 

  • McGonigle C, Collier JS (2014) Interlinking backscatter, grain size and benthic community structure. Estuar Coast Shelf Sci 147:123–136. doi:10.1016/j.ecss.2014.05.025

    Article  Google Scholar 

  • Mitchell NC (1993) A model for attenuation of backscatter due to sediment accumulations and its application to determine sediment thicknesses with GLORIA sidescan sonar. J Geophys Res 98:22477. doi:10.1029/93JB02217

    Article  Google Scholar 

  • Mitchell NC, Hughes Clarke JE (1994) Classification of seafloor geology using multibeam sonar data from the Scotian Shelf. Mar Geol 121:143–160

    Article  Google Scholar 

  • Mitchell JS, Mackay KA, Neil HL, Mackay EJ, Pallentin A, Notman P (2012) Undersea New Zealand, 1:5,000,000. NIWA Chart, Miscellaneous Series No. 92

  • Muller RD, Eagles S, Hogarth P, Hughes M (2007) Automated textural image analysis of seabed backscatter mosaics: a comparison of four methods. Spec Pap Geol Assoc Canada 47:39–57

    Google Scholar 

  • Nodder S (2012) Appendix 12: natural sedimentation on the Chatham Rise. National Institute of Water and Atmospheric Research, Wellington

    Google Scholar 

  • Pomar L (1983) High-resolution sequence stratigraphy in prograding miocene carbonates: application to seismic interpretation. In: Loucks RG, Sarg JF (eds) Carbonate sequence stratigraphy: recent developments and applications, AAPG Memoir 57. AAPG, Tulsa, pp 389–407

    Google Scholar 

  • Powers DMW (2011) Technical report SIE-07-001: evaluation: from precision, recall and F-factor to ROC, informedness, markedness and correlation. J Mach Learn Technol 2:37–63

    Google Scholar 

  • Preston J (2009) Automated acoustic seabed classification of multibeam images of Stanton Banks. Appl Acoust 70:1277–1287. doi:10.1016/j.apacoust.2008.07.011

    Article  Google Scholar 

  • Rzhanov Y, Fonseca L, Mayer L (2012) Construction of seafloor thematic maps from multibeam acoustic backscatter angular response data. Comput Geosci 41:181–187. doi:10.1016/j.cageo.2011.09.001

    Article  Google Scholar 

  • Schimel A, Beaudoin J, Gaillot A et al (2015) Chap. 7: processing backscatter data: from datagrams to angular responses and mosaics. In: Lurton X, Lamarche G (eds) Backscatter measurements by seafloor-mapping sonars: guidelines and recommendations. Geohab Report, 133–164. ​http://geohab.org/publications/

    Google Scholar 

  • Schneider von Deimling J, Weinrebe W, Tóth Z et al (2013) A low frequency multibeam assessment: spatial mapping of shallow gas by enhanced penetration and angular response anomaly. Mar Pet Geol 44:217–222

    Article  Google Scholar 

  • Schneider von Deimling J, Held P, Feldens P, Wilken D (2016) Effects of using inclined parametric echosounding on sub-bottom acoustic imaging and advances in buried object detection. Geo-Marine Lett 36:113–119. doi:10.1007/s00367-015-0433-3

    Article  Google Scholar 

  • Simons DG, Snellen M (2009) A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data. Appl Acoust 70:1258–1268. doi:10.1016/j.apacoust.2008.07.013

    Article  Google Scholar 

  • Simons DG, Snellen M, Michael A (2007) A multivariate correlation analysis of high frequency bottom backscattering strength measurements with geo-technical parameters. IEEE J Ocean Eng 32:640–650

    Article  Google Scholar 

  • Smith WHF, Wessel P (1990) Gridding with continuous curvature splines in tension. Geophysics 55:293. doi:10.1190/1.1442837

    Article  Google Scholar 

  • Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62:77–89. doi:10.1016/S0034-4257(97)00083-7

    Article  Google Scholar 

  • Stephens D, Diesing M (2014) A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data. PLoS One 9:e93950. doi:10.1371/journal.pone.0093950

    Article  Google Scholar 

  • Uenzelmann-Neben G, Grobys J, Gohl K, Barker D (2009) Neogene sediment structures in bounty trough, eastern New Zealand: influence of magmatic activity and oceanic current activity. GSA Bull 121:134–139

    Google Scholar 

  • Weber T, Lurton X (2015) Chap. 2: background and fundamentals. In: Lurton X, Lamarche G (eds) Backscatter measurements by seafloor-mapping sonars: guidelines and recommendations. Geohab Report, 25–52. http://geohab.org/publications/

    Google Scholar 

  • Wood RA, Andrews PB, Herzer RH (1989) Cretaceous and cenozoic geology of the Chatham Rise region, South Island, New Zealand. New Zealand Geological Survey, Lower Hutt

    Google Scholar 

  • Yu J, Marsh I, Brown C, Henrys SA (2010) Modelling and inversion of multibeam backscatter from a rough seafloor. GeoHab 2010 Conference. Wellington

    Google Scholar 

  • Zampetti V, Schlager W, van Konijnenburg J-H, Everts A-J (2004) Architecture and growth history of a Miocene carbonate platform from 3D seismic reflection data; Luconia province, offshore Sarawak, Malaysia. Mar Pet Geol 21:517–534. doi:10.1016/j.marpetgeo.2004.01.006

    Article  Google Scholar 

  • Zhi H, Siwabessy J, Nichol SL, Brooke BP (2014) Predictive mapping of seabed substrata using high-resolution multibeam sonar data: a case study from a shelf with complex geomorphology. Mar Geol 357:37–52

    Article  Google Scholar 

Download references

Acknowledgements

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.

Author information

Authors and Affiliations

  1. Department of Geology, University of Otago, PO Box 56, Dunedin, 9054, New Zealand

    Jess I. T. Hillman & Andrew R. Gorman

  2. National Institute of Water and Atmospheric Research, 301 Evans Bay Parade, Hataitai, Wellington, 6021, New Zealand

    Geoffroy Lamarche & Arne Pallentin

  3. GNS Science, PO Box 30-368, Lower Hutt, 5040, New Zealand

    Ingo A. Pecher

  4. School of Environment, University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand

    Ingo A. Pecher

  5. Christian-Albrechts-University of Kiel, Otto-Hahn-Platz 1, 24118, Kiel, Germany

    Jens Schneider von Deimling

Authors
  1. Jess I. T. Hillman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Geoffroy Lamarche
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arne Pallentin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ingo A. Pecher
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Andrew R. Gorman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jens Schneider von Deimling
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jess I. T. Hillman.

Appendix 1: Backscatter equations

Appendix 1: Backscatter equations

Target strength (TS) is defined as the ratio of the intensity sent by the target back towards the transmitting array (I bs ) by the incident intensity (I i ), as:

$$TS=10\log \left( \frac{{{I}_{bs}}}{{{I}_{i}}} \right)$$
(2)

To obtain BS values, TS must be normalised for the ensonified area, A.

$$BS=TS-10{{\log }_{10}}\left( A \right)$$
(3)

The surface backscattering level can be determined using a statistical geometrical model of scattering at a rough interface, combined with a reflection coefficient that is characteristic of the substrate (Lurton 2010). This can be described by the following equation:

$$EL = SL - 40{\log _{10}}r - 2\alpha r + 10{\log _{10}}\left( {r{\phi}\frac{{cT}}{{2\sin \theta }}} \right) + B{S_s}\left( \theta \right)$$
(4)

where EL is backscatter Echo Level, expressed as a function of the signal level (SL) range (r), signal duration (T), beam aperture \(\left( {\phi} \right)\), absorption coefficient (α), and interface backscatter strength BS s (θ) at incidence angle θ.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hillman, J.I.T., Lamarche, G., Pallentin, A. et al. Validation of automated supervised segmentation of multibeam backscatter data from the Chatham Rise, New Zealand. Mar Geophys Res 39, 205–227 (2018). https://doi.org/10.1007/s11001-016-9297-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11001-016-9297-9

Keywords

Search

Navigation