Model inversion for midwater multibeam backscatter data analysis

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. Furthe- more, 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 spatiotemporal 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.