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Acoustic classification of zooplankton

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dc.contributor.author Martin Traykovski, Linda V.
dc.coverage.spatial Georges Bank
dc.coverage.spatial Gulf of Maine
dc.date.accessioned 2012-08-30T14:06:13Z
dc.date.available 2012-08-30T14:06:13Z
dc.date.issued 1998-02
dc.identifier.uri http://hdl.handle.net/1912/5351
dc.description Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution February 1998 en_US
dc.description.abstract Work on the forward problem in zooplankton bioacoustics has resulted in the identification of three categories of acoustic scatterers: elastic-shelled (e.g. pteropods), fluid-like (e.g. euphausiids), and gas-bearing (e.g. siphonophores). The relationship between backscattered energy and animal biomass has been shown to vary by a factor of —19,000 across these categories, so that to make accurate estimates of zooplankton biomass from acoustic backscatter measurements of the ocean, the acoustic characteristics of the species of interest must be well-understood. This thesis describes the development of both feature based and model based classification techniques to invert broadband acoustic echoes from individual zooplankton for scatterer type, as well as for particular parameters such as animal orientation. The feature based Empirical Orthogonal Function Classifier (EOFC) discriminates scatterer types by identifying characteristic modes of variability in the echo spectra, exploiting only the inherent characteristic structure of the acoustic signatures. The model based Model Parameterisation Classifier (MPC) classifies based on correlation of observed echo spectra with simplified parameterisations of theoretical scattering models for the three classes. The Covariance Mean Variance Classifiers (CMVC) are a set of advanced model based techniques which exploit the full complexity of the theoretical models by searching the entire physical model parameter space without employing simplifying parameterisations. Three different CMVC algorithms were developed: the Integrated Score Classifier (ISC), the Pairwise Score Classifier (PSC) and the Bayesian Probability Classifier (BPC); these classifiers assign observations to a class based on similarities in covariance, mean, and variance, while accounting for model ambiguity and validity. These feature based and model based inversion techniques were successfully applied to several thousand echoes acquired from broadband (-350 kHz - 750 kHz) insonifications of live zooplankton collected on Georges Bank and the Gulf of Maine to determine scatterer class. CMVC techniques were also applied to echoes from fluid-like zooplankton (Antarctic krill) to invert for angle of orientation using generic and animal-specific theoretical and empirical models. Application of these inversion techniques in situ will allow correct apportionment of backscattered energy to animal biomass, significantly improving estimates of zooplankton biomass based on acoustic surveys. en_US
dc.description.sponsorship Thanks to the WHOI/MIT Joint Program Education Office for partial funding. Other sources of funding for my thesis work include the Ocean Acoustics, Oceanic Biology and URIP programs of the Office of Naval Research grant numbers N00014-89- J-1729, N00014-95-1-0287 and N00014-924-1527, and the Biological Oceanography program of the National Science Foundation grant number OCE-9201264.
dc.format.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Massachusetts Institute of Technology and Woods Hole Oceanographic Institution en_US
dc.relation.ispartofseries WHOI Theses en_US
dc.subject Underwater acoustics en_US
dc.subject Zooplankton en_US
dc.title Acoustic classification of zooplankton en_US
dc.type Thesis en_US
dc.identifier.doi 10.1575/1912/5351


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