Marine Geophysical Research

, Volume 39, Issue 1–2, pp 23–40 | Cite as

User expectations for multibeam echo sounders backscatter strength data-looking back into the future

  • Vanessa Lucieer
  • Marc Roche
  • Koen Degrendele
  • Mashkoor Malik
  • Margaret Dolan
  • Geoffroy Lamarche
Original Research Paper


With the ability of multibeam echo sounders (MBES) to measure backscatter strength (BS) as a function of true angle of insonification across the seafloor, came a new recognition of the potential of backscatter measurements to remotely characterize the properties of the seafloor. Advances in transducer design, digital electronics, signal processing capabilities, navigation, and graphic display devices, have improved the resolution and particularly the dynamic range available to sonar and processing software manufacturers. Alongside these improvements the expectations of what the data can deliver has also grown. In this paper, we identify these user-expectations and explore how MBES backscatter is utilized by different communities involved in marine seabed research at present, and the aspirations that these communities have for the data in the future. The results presented here are based on a user survey conducted by the GeoHab (Marine Geological and Biological Habitat Mapping) association. This paper summarises the different processing procedures employed to extract useful information from MBES backscatter data and the various intentions for which the user community collect the data. We show how a range of backscatter output products are generated from the different processing procedures, and how these results are taken up by different scientific disciplines, and also identify common constraints in handling MBES BS data. Finally, we outline our expectations for the future of this unique and important data source for seafloor mapping and characterisation.


Multibeam acoustics Backscatter Habitat mapping Marine geology Seafloor facies 



V. Lucieer was supported by the Marine Biodiversity Hub through funding from the Australian Government’s National Environmental Science Programme. The authors wish to acknowledge the two anonymous reviewers for their constructive comments and to X. Lurton for his edits, which have substantially improved this manuscript.

Supplementary material

11001_2017_9316_MOESM1_ESM.pdf (65 kb)
Supplementary material 1 (PDF 64 KB)


  1. Alexandrou D, Demoustier C (1988) Adaptive noise canceling applied to sea beam sidelobe interference rejection. IEEE J Ocean Eng 13(2):70–76CrossRefGoogle Scholar
  2. APL (1994) Applied physics laboratory; high-frequency ocean environmental acoustic models. APL-UW TR 9407-AEAS 9501. U. o. Washington, USAGoogle Scholar
  3. Bertels L, Houthuys R, Deronde B, Janssens R, Verfaillie E, Van Lancker V (2012) Integration of optical and acoustic remote sensing data over the backshore-foreshore-nearshore continuum: a case study in Ostend (Belgium). J Coast Res 28(6):1426–1436CrossRefGoogle Scholar
  4. Brown CJ, Blondel P (2009) Developments in the application of multibeam sonar backscatter for seafloor habitat mapping. Appl Acoust 70:1242–1247CrossRefGoogle Scholar
  5. Brown CJ, Smith SJ, Lawton P, Anderson JT (2011) Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuar Coast Shelf Sci 92(3):502–520CrossRefGoogle Scholar
  6. Carmichael DR, Linnett LM, Clarke SJ, Calder BR (1996) Seabed classification through multifractal analysis of sidescan sonar images. Proc Inst Electron Eng Radar Sonar Navig 143:140–148CrossRefGoogle Scholar
  7. Colenutt A, Mason T, Cocuccio A, Kinnear R, Parker D (2013) Nearshore substrate and marine habitat mapping to inform marine policy and coastal management. J Coast Res 65(sp2):1509–1514CrossRefGoogle Scholar
  8. Collier JS, Brown CJ (2005) Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Mar Geol 214(4):431–449CrossRefGoogle Scholar
  9. Dartnell P, Gardner JV (2004) Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogramm Eng Remote Sens 70(9):1081–1091CrossRefGoogle Scholar
  10. Davies J, Baxter J, Bradley M, Connor D, Khan J, Murray E, Sanderson W, Turnbull C, Vincent M (2001) Marine monitoring handbook. U. Government, UK, Joint nature conservation committee, p 405Google Scholar
  11. de Campos Carvalho R, de Oliveira AM Jr., Clarke JEH (2013) Proper environmental reduction for attenuation in multi-sector sonars. In: Acoustics in underwater geosciences symposium (RIO acoustics). IEEE/OES, Rio, pp 1–6Google Scholar
  12. de Moustier C (1985) Inference of maganese nodule coverage from sea beam acoustic backscattering data. Geophysics 50:989–1001CrossRefGoogle Scholar
  13. de Moustier C (1986) Beyond bathymetry: mapping acoustic backscattering from the deep seafloor with sea beam. J Acoust Soc Am 79:316–331CrossRefGoogle Scholar
  14. Diesing M, Green SL, Stephens D, Lark RM, Stewart HA, Dove D (2014) Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches. Cont Shelf Res 84(0):107–119CrossRefGoogle Scholar
  15. Diesing M, Mitchell P, Stephens D (2016) Image-based seabed classification: what can we learn from terrestrial remote sensing? ICES J Marine Sci 73(10):2425–2441CrossRefGoogle Scholar
  16. Diez S, Sorribas J (2017) Bedform mapping: multibeam data processing, metadata and spatial data services. Atlas of Bedforms in the Western Mediterranean, Springer, New York, pp 3–6Google Scholar
  17. Faure K, Greinert J, Pecher IA, Graham IJ, Massoth GJ, De Ronde CE, Wright IC, Baker ET, Olson EJ (2006) Methane seepage and its relation to slumping and gas hydrate at the Hikurangi margin, New Zealand. N Z J Geol Geophys 49(4):503–516CrossRefGoogle Scholar
  18. Fonseca L, Mayer L (2007a) Remote esimation of surficial seafloor properties through the applicaiton of angular range analysis to multibeam sonar data. Marine Geophys Res 28:119–126CrossRefGoogle Scholar
  19. Fonseca L, Mayer L (2007b) Remote estimation of surficial seafloor properties through the application angular range analysis to multibeam sonar data. Mar Geophys Res 28:119–129CrossRefGoogle Scholar
  20. Fonseca L, Mayer L, Kraft B (2005) Seafloor Characterization through the application of AVO analysis to multibeam sonar data. Boundary influences in high frequency, shallow water acoustics. N. G. P. a. P. Blondel. University of Bath, UK, pp 241–250Google Scholar
  21. Fonseca L, Brown C, Calder B, Mayer L, Rzhanov Y (2009) Angular range analysis of acoustic themes from Stanton Banks Ireland: a link between visual interpretation and multibeam echosounder angular signatures. Appl Acoust 70:1298–1304CrossRefGoogle Scholar
  22. Foster SD, Hosack GR, Hill NA, Barrett NS, Lucieer VL (2014) Choosing between strategies for designing surveys: autonomous underwater vehicles. Methods Ecol Evol 5(3):287–297CrossRefGoogle Scholar
  23. Goff JA, Olson HC, Duncan CS (2000) Correlation of side-scan backscatter inensity with grain-size distribution of shelf sediments, New Jersey margin. Geo-Mar Lett 20:43–49CrossRefGoogle Scholar
  24. Gueriot D, Chedru J, Daniel S, Maillard E (2000) The patch test: a comprehensive calibration tool for multibeam echosounders. In: TS/IEEE oceans conference and exhibition on where marine science and technology meet, vol 3, pp 1655–1661Google Scholar
  25. Hamid U, Qamar RA, Waqas K (2014) Performance comparison of time-domain and frequency-domain beamforming techniques for sensor array processing. In: 11th international Bhurban conference on applied sciences and technology (IBCAST), 2014Google Scholar
  26. Hamilton LJ, Parnum I (2011) Acoustic seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves. Cont Shelf Res 31:138–148CrossRefGoogle Scholar
  27. Hasan RC, Ierodiaconou D, Laurenson L (2012) Combining angular response classification and backscatter imagery segmentation for benthic biological habitat mapping. Estuar Coast Shelf Sci 97:1–9CrossRefGoogle Scholar
  28. Hellequin L, Boucher JM, Lurton X (2003) Processing of high-frequency multibeam echo sounder data for seafloor characterisation. IEEE J Ocean Eng 28(1):78–89CrossRefGoogle Scholar
  29. Hill NA, Lucieer V, Barrett NS, Anderson TJ, Williams SB (2014) Filling the gaps: predicting the distribution of temperate reef biota using high resolution biological and acoustic data. Estuar Coast Shelf Sci 147:137–147CrossRefGoogle Scholar
  30. Huang Z, Siwabessy J, Nichol S, Anderson T, Brooke B (2013) Predictive mapping of seabed cover types using angular response curves of multibeam backscatter data: testing different feature analysis approaches. Cont Shelf Res 61:12–22CrossRefGoogle Scholar
  31. Hughes-Clarke J (2012) Optimal use of multibeam technology in the study of shelf morphodynamics. Sediments, morphology and sedimentary processes on continental shelves: advances in technologies. Int Assoc Sedimentol Spec Publ 44:3–28Google Scholar
  32. Hughes-Clarke JE, Danforth BW, Valentine P (1997) Aerial seabed classification using backscatter angular response at 95 kHz. Shallow water, NATO SACLANTCEN, conference proceedings series CP, La Spezia, ItalyGoogle Scholar
  33. Imen K, Fablet R, Boucher JM, Augustin JM (2005) Statistical discrimination of seabed textures in sonar images using co occurrence statistics. IEEE oceans’2005 conference proceedings, Brest, FranceGoogle Scholar
  34. Jackson DR, Richardson MD (2007) High frequency seafloor acoustics. Springer, New YorkCrossRefGoogle Scholar
  35. Jackson DR, Winebrenner DP, Ishimaru A (1986) Application of the composite roughness model to high-frequency bottom backscatter. J Acoust Soc Am 79:1410–1422CrossRefGoogle Scholar
  36. Kraft BJ, Fonseca L, Mayer LA, McGillicuddy G, Ressler J, Henderson J, Simpkin PG (2004) In situ measurement of sediment acoustic properties and relationship to multibeam backscatter. J Acoust Soc Am 115(5):2401–2402CrossRefGoogle Scholar
  37. Lamarche G, Lurton X, Verdier A-L, Augustin J-M (2011a) Quantitative characterisation of seafloor substrate and bedforms using advanced processing of multibeam backscatter application to Cook Strait New Zealand. Cont Shelf Res 31:S93–S109CrossRefGoogle Scholar
  38. Lamarche G, Lurton X, Verdier AL, Augustin JM (2011b) Quantitative characterisation of seafloor substrate and bedforms using advanced processing of multibeam backscatter-application to Cook Strait, New Zealand. Cont Shelf Res 31(2):S93–S109CrossRefGoogle Scholar
  39. Le Chenadec G, Boucher JM, Lurton X (2007) Angular dependence of K-distributed sonar data. IEEE Trans Geosci Remote Sens 45(5):1224–1235CrossRefGoogle Scholar
  40. Lechner AM, Langford WT, Jones SD, Bekessy SA, Gordon A (2012) Investigating species-environment relationships at multiple scales: differentiating between intrinsic scale and the modifiable areal unit problem. Ecol Complex 11:91–102CrossRefGoogle Scholar
  41. Lecours V (2016) Quantifying the effects of variable selection, spatial scale and spatial data quality in marine benthic habitat mapping. Doctor of Philosophy, Memorial University of NewfoundlandGoogle Scholar
  42. Linnett LM, Clarke SJ, Graham C, Langhorne DN (1991) Remote sensing of the seabed using fractal techniques. Electron Commun Eng J 3(5):195–203CrossRefGoogle Scholar
  43. Lucieer VL (2007) The application of automated segmentation methods and fragementation statistics to characterise rocky reef habitat. J Spatial Sci 52(1):81–91CrossRefGoogle Scholar
  44. Lucieer VL (2008) Object-oriented classification of sidescan sonar data for mapping benthic marine habitats. Int J Remote Sens 29(3):905–921CrossRefGoogle Scholar
  45. Lucieer V, Lamarche G (2011a) Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Cont Shelf Res 31(11):1236–1247CrossRefGoogle Scholar
  46. Lucieer VL, Lamarche G (2011b) Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Cont Shelf Res 31(11):1236–1247CrossRefGoogle Scholar
  47. Lucieer V, Hill NA, Barrett NS, Nichol S (2013) Do marine substrates ‘look’ and ‘sound’ the same? Supervised classification of multibeam acoustic data using autonomous underwater vehicle images. Estuar Coast Shelf Sci 117:94–106CrossRefGoogle Scholar
  48. Lucieer VL, Siwabessy JW, Huang Z, Hayes K (2014) Multi-scale image segmentation of multibeam backscatter data for benthic monitoring. Geohab 2014. I. Daniel and N. Scott Lorne. Deakin University, Australia, p 62Google Scholar
  49. Lucieer V, Huang Z, Siwabessy J (2016) Analyzing uncertainty in multibeam bathymetric data and the impact on derived seafloor attributes. Mar Geodesy 39(1):32–52CrossRefGoogle Scholar
  50. Lurton X (2010) An introduction to underwater acoustics. Principles and applications, 2nd edn. Springer, New YorkCrossRefGoogle Scholar
  51. Lurton X, Lamarche G (2015) Backscatter measurements by seafloor mapping sonars: guidelines and recommendations.
  52. Lurton X, Dugelay S, Augustin JM (1994) Analysis of multibeam echo sounder signals from the deep seafloor, Brest, FranceGoogle Scholar
  53. Lurton X, Lamarche G, Brown C, Lucieer VL, Rice G, Schimel A, Weber T (2015) Backscatter measurements by seafloor mapping sonars: guidelines and recommendations. A collective report by members of the GeoHab Backscatter Working Group (May), pp 1–200Google Scholar
  54. Malik MA, Mayer LA, Weber TC, Calder BR, Huff LC (2013) Challenges of defining uncertainty in multibeam sonar derived seafloor backscatter. International underwater acoustic conference and exhibition, Corfu, GreeceGoogle Scholar
  55. Müller RD, Qin X, Sandwell DT, Dutkiewicz A, Williams SE, Flament N, Maus S, Seton M (2016) The GPlates portal: cloud-based interactive 3D visualization of global geophysical and geological data in a web browser. PLoS ONE 11(3):e0150883CrossRefGoogle Scholar
  56. Pace NG, Dyer CM (1979) Machine classification of sedimentary sea bottoms. IEEE Trans Geosci Electron 17(3):52–56CrossRefGoogle Scholar
  57. Pace NG, Gao H (1988) Swath seabed classification. IEEE J Ocean Eng 13(2):83–89CrossRefGoogle Scholar
  58. Parnum IM (2007) Benthic habitat mapping using multibeam sonar system. Ph.D. Curtin University of TechnologyGoogle Scholar
  59. Reed DL, Hussong DM (1989) Digital image processing techniques for enhancement and classification of SeaMARC II side scan sonar imagery. J Geophys Res 84(6):7469–7490CrossRefGoogle Scholar
  60. Rice G, Cooper R, Degrendele K, Gutierrez F, Le Bouffant N, Roche M (2015) Chapter 5: acquisition: best practice guide. In: Lurton X, Lamarche G (eds) Backscatter measurements by seafloor-mapping sonars: guidelines and recommendations. Geohab Report, pp 79–132Google Scholar
  61. Roche M, Baeye M, De Bisschop J, Degrendele K, De Mol L, Papili S, Lopera O, Van Lancker V (2015) Backscatter stability and influence of water column conditions: estimation by multibeam echosounder and repeated oceanographic measurements Belgian part of the North Sea. Institute of AcousticsGoogle Scholar
  62. Schimel A, Beaudoin J, Gaillot A, Keith G, Le Bas T, Parnum I, Schmidt V (2015) Chapter 7: processing backscatter data: from datagrams to angular responses and mosaics. Lurton X, Lamarche G (eds) Backscatter measurements by seafloor-mapping sonars: guidelines and recommendations. Geohab Report, pp 133–164.
  63. Simons DG, Snellen M (2009) A Bayesian approach to seafloor classification using multibeam echosounder backscatter data. Appl Acoust 2009:1258–1268CrossRefGoogle Scholar
  64. Stanton TK (1984) Volume scattering: echo peak PDF. J Acoust Soc Am 75(S1):S51CrossRefGoogle Scholar
  65. Tamsett D (1993) Seabed characterization and classification from the power spectra of side scan sonar data. Mar Geophys Res 15:43–64CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Vanessa Lucieer
    • 1
  • Marc Roche
    • 2
  • Koen Degrendele
    • 2
  • Mashkoor Malik
    • 3
  • Margaret Dolan
    • 4
  • Geoffroy Lamarche
    • 5
  1. 1.Institute for Marine and Antarctic StudiesUniversity of TasmaniaHobartAustralia
  2. 2.FPS Economy, Continental Shelf ServiceBrusselsBelgium
  3. 3.Office of Ocean Exploration and ResearchNOAAMarylandUSA
  4. 4.Geological Survey of NorwayTrondheimNorway
  5. 5.National Institute of Water and AtmosphereGreta PointWellingtonNew Zealand

Personalised recommendations