Abstract
Acoustic backscatter from the seafloor is a complex function of signal frequency, seabed roughness, grain size distribution, benthos, bioturbation, volume reverberation, and other factors. Angular response is the variation in acoustic backscatter with incident angle and is considered be an intrinsic property of the seabed. An unsupervised classification technique combining a self-organising map (SOM) and hierarchical clustering was used to create an angular response facies map and explore the relationships between acoustic facies and ground truth data. Cluster validation routines indicated that a two cluster solution was optimal and separated sediment dominated environments from mixtures of sediment and hard ground. Low cluster separation limited cluster validation routines from identifying fine cluster structure visible with an AR density plot. Cluster validation, aided by a visual comparison with an AR density plot, indicated that a 14 cluster solution was also a suitable representation of the input dataset. Clusters that were a mixture of hard and unconsolidated substrates displayed an increase in backscatter with an increase in the occurrence of hard ground and highlighted the sensitivity of AR curves to the presence of even modest amounts of hard ground. Remapping video observations and sediment data onto the SOM matrix is innovative and depicts the relationship between ground truth data and cluster structure. Mapping environmental variables onto the SOM matrix can show broad trends and localised peaks and troughs and display the variability of ground truth data within designated clusters. These variables, when linked to AR curves via clusters, can indicate how environmental factors influence the shape of the curves. Once these links are established they can be incorporated into improved geoacoustic models that replicate field observations.
Similar content being viewed by others
References
Briggs KB, Williams KL, Jackson DR, Jones CD, Ivakin AN, Osri TH (2002) Fine-scale sedimentary structure: implications for acoustic remote sensing. Mar Geol 182:141–159
Brock G, Pihur V, Datta S, Datta S (2008) ClValid: an R package for cluster validation. J Stat Softw 25(4):1–22
Brooke B, Nichol S, Hughes M, McArthur M, Anderson T, Przeslawski R, Siwabessy J, Heyward A, Battershill C, Colquhoun J, Doherty P (2009) Carnarvon Shelf Survey Post-cruise Report. Geoscience Australia, Record 2009/02, Canberra, Australia
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 92:502–520
Calvert J, Strong JA, Service M, McGonigle C, Quinn R (2014) An evaluation of the supervised and unsupervised classification techniques for benthic marine habitat mapping using multibeam echosounder data. ICES J Mar Sci. doi:10.1093/icesjms/fsu223
Che Hasan R, Ierodiaconou D, Laurenson L, Schimel A (2014) Integrating multibeam backscatter angular response mosaic and bathymetry data for benthic habitat mapping. PLoS ONE 9(5), e97339. doi:10.1371/journal.pone.0097339
De Falco D, Tonielli R, Di Martino G, Innangi S, Simeone S, Parnum IM (2010) Relationships between multibeam backscatter, sediment grain size and Posidonia oceanica seagrass distribution. Cont Shelf Res 30:1941–1950
de Moustier CP, Alexandrou D (1991) Angular dependence of 12-kHz seafloor acoustic backscatter. J Acoust Soc Am 90(1):522–531
de Moustier C, Matsumoto H (1993) Seafloor acoustic remote sensing with multibeam echo-sounders and bathymetric sidescan sonar systems. Mar Geophys Res 15:27–42
Dunn JC (1974) Well separated clusters and fuzzy partitions. J Cybernet 4:95–104
Ferrini VL, Flood RD (2006) The effects of fine-scale surface roughness and grain size on 300kHz multibeam backscatter intensity in sandy marine sedimentary environments. Mar Geol 228:153–172
Fonseca L, Calder BR (2007) Clustering acoustic backscatter in the angular response space. U.S. Hydrographic Conference (US HYDRO), Norfolk
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
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(10):1289–1304
Friedman A, Pizarro O, Williams SB, Johnson-Roberson M (2012) Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions. PLoS ONE 7(12):e50440. doi:10.1371/journal.pone.0050440
Gavrilov AN, Duncan AJ, McCauley RD, Parnum IM, Penrose JD, Siwabessy PJW, Woods AJ, Tseng YT (2005b) Characterization of the seafloor in Australia’s coastal zone using acoustic techniques. Proceedings of the International Conference in Underwater Acoustic Measurements: Technologies & Results, Crete, Greece
Goff JA, Olson HC, Duncan CS (2000) Correlation of sidescan backscatter intensity with grain-size distribution of shelf sediments, New Jersey margin. Geo-Mar Lett 20:43–49
Hamilton EL (1970) Sound velocity and related properties of marine sediments, North Pacific. J Geophys Res 75:4423–4446
Hamilton LJ, Parnum I (2011) Acoustic Seabed segmentation from direct statistical clustering of entire multibeam sonar backscatter curves. Cont Shelf Res 31:138–148
Hamilton EL, Shumway G, Menard HW, Shipek CJ (1956) Acoustic and physical properties of shallow-water sediments off San Diego. J Acoust Soc Am 28:1–15
Handl J, Knowles J, Kell DB (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15):3201–3212
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 61–62:12–22
Huang Z, 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
Hughes Clarke JE, Danforth BW, Valentine P (1997) Aerial seabed classification using backscatter angular response at 95kHz. Shallow Water, NATO SACLANTCEN, Conference Proceedings Series CP, vol. 45 pp 243-250
Jackson DR, Winebrenner DP, Ishimaru A (1986) Application of the composite roughness model to high-frequency bottom backscatter. J Acoust Soc Am 79:1410–1422
Jackson DR, Briggs KB, Williams KL, Richardson MD (1996) Tests of models for high-frequency seafloor backscatter. IEEE J Ocean Eng 21(4):458–470
Johnson-Roberson M, Pizarro O, Williams SB, Mahon I (2010) Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys. J Field Robot 27:21–51
Kaufman L, Rousseeuw PJ (1990) Finding groups in data. Wiley, New York
Kohonen T (1990) The self-organising Map. Proc IEEE 78(9):1464–1480
Kohonen T, Hynninen J, Kangas J, Laaksonen J (1996) SOM_PAK: the self-organising-map program package. Helsinki University of Technology, Laboratory of Computer and Information Science, Technical Report A31, Helsinki
Lamarche G, Lurton X, Verdier A, Augustin J (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
Lyons AP, Anderson AL, Dwan FS (1994) Acoustic scattering from the seafloor: modelling and data comparison. J Acoust Soc Am 95(5):2441–2451
Müller G, Gastner M (1971) The “Karbonat-Bombe”, a simple device for the determination of the carbonate content in sediments, soils, and other materials. Neues Jahrbuch Mineral 10:466–469
Nichol SL, Brooke BP (2011) Shelf habitat distribution as a legacy of late quaternary marine transgressions: a case study for a tropical carbonate province. Cont Shelf Res 31:1845–1857
Parnum I (2007) Benthic habitat mapping using multibeam sonar systems. Ph.D. Thesis, Curtin University of Technology, Western Australia
R Development Core Team (2006) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Rousseeuw PJ (1987) Silhouettes: a graphical Aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65
Rzhanov Y, Fonseca L, Mayer L (2012) Construction of seafloor thematic maps from multibeam acoustic backscatter angular response. Comput Geosci 41:181–187
Seiler J, Friedman AL, Steinberg D, Barrett N, Williams A, Holbrook NJ (2012) Image-based continental shelf habitat mapping using novel automated data extraction techniques. Cont Shelf Res 45:87–97
Stein DL, Tissot BN, Hixon MA, Barss WH (1992) Fish–habitat associations on a deep reef at the edge of the Oregon continental shelf. Fish Bull 90:540–551
Talukdar KK, Tyce RC, Clay CS (1995) Interpretation of Sea Beam backscatter data collected at the Laurentian fan off Nova Scotia using acoustic backscatter theory. J Acoust Soc Am 97(3):1545–1558
Ultsch A, Siemon HP (1990) Kohonen’s self-organising feature maps for exploratory data analysis. Proceedings INNC90, International Neural Network Conference, pp 305-308
Urick RJ (1983) Principles of underwater sound, 3rd edn. Peninsula, Los Altos
Vesanto J, Alhoniemi E (2000) Clustering of the self-organising Map. IEEE Trans Neural Netw 11(3):586–600
Williams KL, Jackson DR, Thorsos EI, Tang D, Briggs KB (2002) Acoustic backscattering experiments in a well characterized sand sediment: data/model comparisons using sediment fluid and Biot models. IEEE J Ocean Eng 27(3):376–387
Acknowledgments
The data used for this study was collected by the Marine Biodiversity Hub, funded through the Commonwealth Environment Research Facilities (CERF) program, an Australian Government initiative supporting world class, public good research. We thank the master and crew of the RV Solander and scientific staff at the Australian Institute of Marine Science (AIMS) for their support in conducting the survey. We also thank Ian Atkinson, Cameron Buchanan, Mike Sexton and Stephen Hodgkin for expert support with multibeam sonar data acquisition, and Tara Anderson for characterisation of towed video used in this paper. We kindly thank two anonymous reviewers for their detailed and constructive reviews of this manuscript. JS, BB and SLN publish with permission of the Chief Executive Officer, Geoscience Australia.
Conflict of interest
The authors declare that they have no conflict of interest.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Daniell, J., Siwabessy, J., Nichol, S. et al. Insights into environmental drivers of acoustic angular response using a self-organising map and hierarchical clustering. Geo-Mar Lett 35, 387–403 (2015). https://doi.org/10.1007/s00367-015-0415-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00367-015-0415-5