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Model inversion for midwater multibeam backscatter data analysis
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A model of the multibeam echosounding process was developed. This model has now been used as the basis for the application of a model inversion technique, with the aim of analyzing midwater multibeam echosounder data, for fisheries applications.
Research on midwater multibeam echosounding for
fisheries is in its infancy. Some results have been published, announcing promising progress at the level of multibeam transducer design, beamforming algorithms and calibration procedures, but no standard post-processing technique has emerged yet. In this paper, the post-processing of midwater multibeam backscatter data is placed in a scientific data mining framework. Data mining aims at automatically extracting useful information and knowledge from large volumes of data which don't reveal this knowledge in a trivial manner. Multibeam acoustic data has an additional dimension compared to single beam data, and multibeam echosounding results in large data logging rates, typically several gigabytes per hour, making it suitable for applying data mining algorithms in order to analyze the data in post-processing. A data mining technique to handle
multibeam data sets is presented. The technique is based on
inverse modeling. A model of the multibeam echosounding
process was developed, including a physical underwater
acoustics model, as well as a model of a generic multibeam
transducer and its digital signal processor. This model has
now been approximated by an invertible function, leading to
an inverse model. Applying the inverse model to midwater
multibeam backscatter data results in a set of soundings. A
multibeam midwater sounding is the equivalent of a standard
multibeam sounding as obtained from hydrographic
multibeam instruments. In the midwater multibeam
echosounding context, a sounding can represent anything in
the water column, not just the seabed. These soundings can be visualized directly, allowing for exploratory data analysis in a 3d or 4d interactive environment.
Furthermore, various features can be tagged to each sounding, such as the backscatter energy value and some
statistical parameters of the multibeam ping from which the
sounding was obtained. The term data node is used to
describe the sounding and its associated feature vector. The
set of data nodes serves as the basis for further advanced
spatio-temporal data mining techniques. Soundings can be
clustered into coherent groups, each cluster representing an
object in the water column, such as a fish school. Cluster
features are obtained from the feature tags of their contained data nodes, giving rise to feature vectors for each cluster.
Clusters can be classified into classes of different types, using each cluster's feature vector. When a cluster is thought of as a fish school, it can be classified according to fish species or age group, for example.
The concept of a set of data nodes is a versatile concept
that can be extended further, enabling the application of
more advanced clustering and classification algorithms.
|Item Type:||Conference or Workshop Item (Paper)|
|Date Deposited:||12 Jul 2005|
|Last Modified:||18 Nov 2014 03:10|
|Item Statistics:||View statistics for this item|
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