Neural network for ocean bottom parameter estimation

Robert H. Baran; Hanseok Ko

doi:10.1117/12.138306

16 September 1992 Neural network for ocean bottom parameter estimation

Robert H. Baran, Hanseok Ko

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138306
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Robert H. Baran and Hanseok Ko "Neural network for ocean bottom parameter estimation", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138306

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
12 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Digital filtering

Neural networks

Object recognition

Linear filtering

Autoregressive models

Nonlinear filtering

Differential equations

Show All Keywords

Keywords/Phrases

Search In:

Publication Years