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Proceedings Paper

Neural network for ocean bottom parameter estimation
Author(s): Robert H. Baran; Hanseok Ko
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Paper Abstract

This concerns the estimation of physical parameters characterizing the ocean bottom from parallel time series representing (A) the instantaneous height of the water column above a section of bottom and (B) the vertical displacement of the bottom section from its long term average. Time series A, representing wind-generated waves, is modeled by a sinusoid with phase jitter. In the absence of both seismic background noise and any nonlinear behavior in the ocean bottom, time series B could be modeled by coupling the bottom through a spring and dashpot to a mass proportional to A. We created two- and three-layer adaptive networks in which series A and B (with lag) were inputs. Training consisted in subtracting the network output from the current value of series B and feeding back these errors in accordance with the appropriate formulas for gradient descent in squared error. The trained nets act as models of the way in which the bottom responds to changes at the surface.

Paper Details

Date Published: 16 September 1992
PDF: 12 pages
Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); doi: 10.1117/12.138306
Show Author Affiliations
Robert H. Baran, Naval Surface Warfare Ctr. (United States)
Hanseok Ko, Naval Surface Warfare Ctr. (United States)

Published in SPIE Proceedings Vol. 1700:
Automatic Object Recognition II
Firooz A. Sadjadi, Editor(s)

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