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Scientific data mining for spatio-temporal hydroacoustic data sets

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Buelens, Bart Andre Hendrik Lutgart (2008) Scientific data mining for spatio-temporal hydroacoustic data sets. PhD thesis, University of Tasmania.

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Abstract

Managing natural marine resources for sustainable exploitation of the oceans and
the flora and fauna they contain is a challenging task. Decisions by policy makers
are based on advice from the scientific community. Through surveying and
monitoring programs, scientists study the marine environment to gain insight into
its structure and function. Employing acoustic techniques, sonar systems are often
the best tools available to effectively observe aquatic environments. Important
applications include fisheries and seafloor mapping. Fish stock assessments are
typically conducted using single beam echosounders, while bathymetric surveys are
conducted with multibeam sonar.
Multibeam sonar instruments that are capable of collecting samples for the
complete water column are an emerging technology. Since they collect acoustic
data over much greater sampling volumes than single beam instruments, significant
improvements in fisheries studies are expected. The combined collection of seafloor
and water-column data will lead to survey cost savings and to an integrated,
ecosystem-based approach to monitoring the ocean environment. While standard
data analysis procedures are established for single beam fisheries and standard
multibeam bathymetric applications, this is not the case for full water-column
multibeam sonar data. In this thesis, a data mining approach for handling such data is proposed. The
developed method consists of a preprocessing algorithm based on an inversion
technique, followed by a pattern analysis algorithm using kernel clustering methods.
The preprocessing algorithm applies a deconvolution as a model inversion method
to reduce the data set in size and to convert the acoustic measurements into a
generic vector representation. Each vector has a spatial and a temporal component
as well as a number of additional features typically relating to the acoustic
backscatter energy. These spatio-temporal vectors are then subjected to pattern
analysis algorithms. Two clustering algorithms are selected: a density based spatial
clustering algorithm, and a clustering algorithm based on kernel methods. A new
method is developed to allow the kernel clustering algorithm to make use of the
spatial and non-spatial components of the data in a combined fashion. This results
in a powerful, flexible and versatile clustering procedure. The outcome is a
segmentation of the data into coherent structures, for example fish schools and the
seabed. Classification is achieved through the differentiation between data clusters
indicative of different fish species or seabed habitats. The effectiveness of the data
mining methods is demonstrated in a number of case studies.
It is hoped that the developed approach will facilitate routine use of water-column
multibeam sonar data for fisheries applications in particular, and for ecosystem
studies and marine resource management in general.

Item Type: Thesis (PhD)
Copyright Holders: The Author
Copyright Information:

Copyright 2008 the author

Additional Information:

No access or viewing until 16 November 2010. After that date, available for use in the Library and copying in accordance with the Copyright Act 1968, as amended. Thesis (PhD)--University of Tasmania, 2008. Includes bibliographical references

Date Deposited: 25 Nov 2014 00:57
Last Modified: 11 Mar 2016 05:53
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