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Sediment layer tracking using neural networks

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Date Issued:
1998
Summary:
The detection of sediment layer interfaces in normal incidence acoustic reflection data is a requirement for automatic classification and geologic mapping of subsurface layers. The detection is difficult because of the constructive and destructive interference caused by the impedance changes in the sediment column and high scattering noise levels. The purpose of this work is to implement a procedure using neural networks that automatically detects the sediment layers from the envelope of acoustic reflections. The data was collected using a sub-bottom profiler that transmits a 2 to 10 kHz FM pulse. The detection procedure is a three step method: a first neural network removes most of the reflections due to random scatterers, a second neural network tracks the layers and a third algorithm recognizes the segments of detected layers corresponding to the same sediment interface Applied on different sub-bottom images, the procedure detects more than 80% of the layers correctly.
Title: Sediment layer tracking using neural networks.
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Name(s): Freyermuth, Vincent Nicolas.
Florida Atlantic University, Degree grantor
Schock, Steven G., Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1998
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 100 p.
Language(s): English
Summary: The detection of sediment layer interfaces in normal incidence acoustic reflection data is a requirement for automatic classification and geologic mapping of subsurface layers. The detection is difficult because of the constructive and destructive interference caused by the impedance changes in the sediment column and high scattering noise levels. The purpose of this work is to implement a procedure using neural networks that automatically detects the sediment layers from the envelope of acoustic reflections. The data was collected using a sub-bottom profiler that transmits a 2 to 10 kHz FM pulse. The detection procedure is a three step method: a first neural network removes most of the reflections due to random scatterers, a second neural network tracks the layers and a third algorithm recognizes the segments of detected layers corresponding to the same sediment interface Applied on different sub-bottom images, the procedure detects more than 80% of the layers correctly.
Identifier: 9780591928013 (isbn), 15561 (digitool), FADT15561 (IID), fau:12321 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 1998.
Subject(s): Neural networks (Computer science)
Marine sediments--Acoustic properties
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15561
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.