A digital acoustic seabed classification system, QTC View (Series IV) was used in the coastal waters of Newfoundland to characterize and classify marine benthic habitats. The QTC View system was calibrated in Placentia Bay at sites identified independently during a submersible research program. Four different habitats were used for calibration of the QTC View system: mud, gravel, rock, and macroalgae on rock. These different habitats were used as a ''training'' catalogue for real-time classification of marine habitats carried out in Bonavista Bay. The classification data were based on over 2000 km of survey tracks ranging in depth from approximately 10-m to 220-m depth. Post classification analyses were carried out using data visualization techniques, simultaneously comparing the classification data in mathematical and geographic settings. Following post classification, eight different marine habitats were identified using the acoustic system: mud, loose gravel, gravel, rock, sparse algae/cobble, macroalgae, high relief/deep cobble, and wood chips. Throughout the surveyed area, rock habitat dominated, followed by sparse algae/cobble and high relief/cobble habitat types. The wood chip habitat type was identified within a small area that historically had been associated with logging in coastal Newfoundland.

Two single-beam, seabed-classification systems, QTC VIEW Series IV and QTC VIEW Series V, were used to identify and map biosedimentary gradients in a mid-shelf area off Western Portugal. The survey area has a moderate slope, a depth ranging from 30 to 90 m along a 3.5-km axis perpendicular to the shoreline, and is characterized by smooth sedimentary and biological gradients. Ground truth for sediment grain size and macrofaunal communities was based on grab sampling at 20 sites. The sedimentary and biological data were analysed using classification and ordination techniques. The acoustic data were analysed with qtc impact software and classified into acoustic classes. The affinity groups obtained in each data set were mapped using a Geographics Information System. All showed good agreement and identified prevailing gradients along a northwest–southeast direction. Three acoustic classes were identified, corresponding to the predominant sediment types, namely fine sand with low silt and clay content, silty, very fine sand, and mud. A close relationship with benthic communities was also verified, although less marked because benthic communities continuously change along the northwest–southeast gradient. Overall, the acoustic system coupled with ground-truthing data was able to discriminate and characterize the various benthic biotopes in the survey area.

A 50 kHz echo sounder and a digital waveform acquisition and seabed classification system were used to record and process echoes from embayments near southeast Vancouver Island, Canada. Each echo was preprocessed and several statistical and spectral algorithms were used to extract its characteristic patterns. The patterns were grouped into four acoustic classes. Ground truth was available from each site allowing each cluster to be associated with a seabed type. The sediment properties and acoustic classification were compared using multivariate correlation analysis. The frequency of occurrence of fine sand was found to have large negative correlation with the penetration of a free-fall penetrometer. Fig. 1 Location of study sites, indicated by circles. Inset charts show Royal Roads and Esquimalt Harbour of southern Saanich Peninsula, and Wanoose Harbour, about 15 km northeast of Nanaims. 0-7803-4108-2/97/$10.00 0 1 997 IEEE I . INTRODUCTIONThe amplitude and shape of an acoustic signal reflected from the sea floor are determined mainly by the sea bottom roughness and by the contrast in acoustic impedance between water and bottom. The remote classification of the sea bottom requires an acoustic data acquisition system and a set of algorithms that analyze the data, determine the seabed type and relate the results of the acoustic classification to the physical properties of the marine sediments. Several studies have been conducted recently in the area of experimental recognition of the seabed using a wide-band chirp sonar [ 1,2] or a parametric array [3]. The data processing relies usually on the extraction of characteristic features from the echo. Classification implies some kind of ordination technique to group echoes with similar features. An inversion technique, similar to seismic inversion, could also be used for the seabed discrimination. The modeling of acoustic backscatter from sandy and muddy sediments has been developed recently (see for example, [4] and references therein). The forward models developed can be used as a basis for an iterative inversion algorithm such as a singular value decomposition inversion or various conjugate gradient algorithms. In this study, a more robust statistical approach was used. Compared to inversion techniques, the current approach does not require a detailed model of sound propagation. A 50 kHz echo sounder and a digital waveform acquisition system (ISAH-S) were used to record the echo envelope, and a QTC VIEW Seabed Classification System [5] was used to process the data. Preprocessing of the echo involved sea floor identification and filtering of the records. Several algorithms were used to extract characteristic patterns from the preprocessed individual echoes. Statistical analysis of characteristic patterns yielded a small number of linear combinations of features accounting for most of the variation in the data set. Plotted in the space of three primary indices, the echoes grouped into four clusters through unsupervised classification. Extensive ground truthing was avai...

No abstract

Seabed grain size, shear strength, bearing strength, and porosity were measured at 15 sites, all in bays and harbours around southern Vancouver Island. Sounder echoes at 38 and 200 kHz from the same sites were classified using the QTC VIEWTM technology. This approach uses a feature set generated from the direct echoes by a set of algorithms and then reduced by multivariate analysis, with similar acoustic responses put into the same class. Canonical correlation analysis was used to uncover correlations between two data sets: the frequency of occurrence of each acoustic class, and the four geotechnical variables. Bearing strength was found to be the major contributor to the first geotechnical canonical variable, which correlated with classes from the 38-kHz echoes with a coefficient of 0.94.