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Please use this identifier to cite or link to this item: http://drs.nio.org/drs/handle/2264/4037

Title: Estimation of mean grain size of seafloor sediments using neural network
Authors: De, C.
Chakraborty, B.
Citation: Marine Geophysical Research, vol.33; 2012; 45-53
Issue Date: 2012
Publisher: Springer
Abstract: The feasibility of an artificial neural network based approach is investigated to estimate the values of mean grain size of seafloor sediments using four dominant echo features, extracted from acoustic backscatter data. The acoustic backscatter data were collected using a dual-frequency (33 and 210 kHz) single-beam, normal-incidence echo sounder at twenty locations in the central part of the western continental shelf of India. Statistically significant correlations are observed between the estimated average values of mean grain size of sediments and the ground-truth data at both the frequencies. The results indicate that once a multi-layer perceptron model is trained with back-propagation algorithm, the values of mean grain size can reasonably be estimated in an experimental area. The study also revealed that the consistency among the estimated values of mean grain size at different acoustic frequencies is considerably improved with the neural network based method as compared to that with a model-based approach.
Document type: Journal Article
Copyright: An edited version of this paper was published by Springer. This paper is for R & D purpose and Copyright [2012] Springer.
URI: http://drs.nio.org/drs/handle/2264/4037
Appears in Collections:Geology & Geophysical Sciences

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