Geostatistical and Neural-Net Seafloor Classification at High Resolution and Related Scaling Properties.

Abstract

The objective of my research under this grant is the development of an intelligent system for the automated classification of the seafloor using acoustic data. The work is a contribution to the Acoustical Reverberation Special Research Program (ARSRP). We have developed a method for surface classification, incorporating ideas from the theory of geostatistics (in short, the method has been called Geostatistical classification method). Under this project, we have finalized parameter selection and software development for the automated geostatistical seafloor classification method. Thereafter, we applied this method in a geomorphologic segmentation of the Western Flank of the Mid-Atlantic Ridge at 26 deg. North, the area of geophysical survey under the ARSRP in 1992. High-resolution bathymetric data from the 1993 geophysical experiment were also analyzed and compared to the (low resolution) HYDROSWEEP bathymetric data. Surface structures were found to be scale-dependent.

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Document Details

Document Type
Technical Report
Publication Date
Mar 21, 1997
Accession Number
ADA323741

Entities

People

  • Ute C. Herzfeld

Organizations

  • University of Colorado Boulder

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Acoustics
  • Applied Mathematics
  • Computers
  • Data Analysis
  • Earth Sciences
  • Geography
  • Geology
  • Geophysics
  • Germany
  • Glaciology
  • High Resolution
  • Mathematics
  • New York
  • Remote Sensing
  • Seabed
  • Software Development
  • Topography

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.
  • Oceanography.