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Remote acoustic seafloor characterization using numerical model and statistical based stochastic multifractals

DRS at CSIR-National Institute of Oceanography

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Title Remote acoustic seafloor characterization using numerical model and statistical based stochastic multifractals
 
Creator Chakraborty, B.
Haris, K.
 
Subject remote sensing
high frequency
ocean floor
bottom topography
 
Description The spatial variability of sediment geoacoustic inversion parameters are estimated employing bathymetric systems such as multi-beam echo-sounder (MBES) and dualfrequency single-beam echo-sounder (SBES) operable at 95 kHz and 33/210 kHz, respectively. Relationships among the estimated model parameters (substrate and roughness) are investigated along with the benthic macro-fauna information. Distinct interclass separations between the sediment provinces are evident from the estimated mean grain size M-phi, interface roughness spectral parameter w2, and sediment volume scattering parameter sigma 2. These interclass separations are found to be correlated with biologically active faunal functional group assemblages on the seafloor. On the other hand, the continuous form of seafloor heterogeneity (due to bioturbation, sediment deposition, seafloor seepages, or hydrodynamic processes) has received the most attention and necessitates the application of more versatile statistical techniques for determining seafloor roughness statistics. Therefore, 'stochastic multifractal' approach based technique using MBES seafloor image data has been introduced as an alternative approach in such situation. The application of stochastic multifractal technique is suitable for higher order dimensionality and predictability, which substantiates the hitherto applied numerical inversion model based seafloor characterization.
 
Date 2013-07-10T07:06:16Z
2013-07-10T07:06:16Z
2013
 
Type Conference Article
 
Identifier In "First underwater acoustics conference and exhibition. Ed. by: Papadakis, J.S.; Bjorno, L."; 2013; 1013-1020.
http://drs.nio.org/drs/handle/2264/4322
 
Language en
 
Rights Copyright [2013]. All efforts have been made to respect the copyright to the best of our knowledge. Inadvertent omissions, if brought to our notice, stand for correction and withdrawal of document from this repository.
 
Publisher Institute of Applied & Computational Mathematics