Acoustic seabed classification and correlation analysis of sediment properties by QTC VIEW

Abstract

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...

Horizontal and residual circulations driven by wind stress curl in Tokyo Bay

“…The results of this study suggest that particulate organic matter (POM) could be transported to the center of the bay head by the residual circulation driven by pumping velocity. Okada et al [2009] carried out a field experiment to measure moisture content of the sediment from 2003 to 2004 using Quester tangent [Co., QTC VIEW; Preston et al, 1999, Preston andCollins, 2000;Tsemahman and Collins, 1997]. These measurements were verified by comparison with sediment samples, and the field study demonstrated that sediment with a higher moisture content exists at the center of the bay head.…”

“…Okada et al . [] carried out a field experiment to measure moisture content of the sediment from 2003 to 2004 using Quester tangent [Co., QTC VIEW; Preston et al ., , Preston and Collins , ; Tsemahman and Collins , ]. These measurements were verified by comparison with sediment samples, and the field study demonstrated that sediment with a higher moisture content exists at the center of the bay head.…”

Seabed habitat mapping techniques: an overview of the performance of various systems

“…More recently acoustic ground-discrimination systems (AGDS) have been developed to detect the acoustic-reflectance properties of the seabed. Indeed, the nature of bottom echoes is influenced not only by basic sediment grainsize parameters, sediment sorting, microtopography, sediment density and porosity, but also by the presence, concentration and type of benthic fauna and flora (TSEMAHMAN & COLLINS, 1997;COLLINS & GALLOWAY, 1998;BORNHOLD et al, 1999;HAMILTON et al, 1999;KLOSER et al, 2001;ANDERSON et al, 2002;HUTIN et al, 2005). RoxAnnì (CHIVERS et al, 1990) and QTC VIEWì (COLLINS et al, 1996) are two widely used AGDSs to extract shape, energy or both features contained in the bottom acoustic signals.…”

Unmanned Surface Vehicles for automatic bathymetry mapping and shores' maintenance

“…In [2] a 50 Hz echosounder combined with a digital wave form acquisition and a 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. In [3] a review of the acoustic techniques for the identification, classification and mapping of benthic habitats is given, reporting the commonly employed acoustic devices and subsurface sensing techniques.…”