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Related Experiment Video

Updated: Jul 26, 2025

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools
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Seabed classification and source localization with Gaussian processes and machine learning.

Christina Frederick1, Zoi-Heleni Michalopoulou1

  • 1Department of Mathematical Sciences, New Jersey Institute of Technology, Newark, New Jersey 07102, USA christin@njit.edu, michalop@njit.edu.

JASA Express Letters
|June 13, 2023
PubMed
Summary
This summary is machine-generated.

Gaussian processes enhance acoustic data for superior seabed classification and source range estimation. This machine learning approach improves sediment-range mapping in diverse underwater environments.

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Area of Science:

  • Ocean acoustics
  • Geophysical signal processing
  • Machine learning applications

Background:

  • Workshop '97 acoustic data provide insights into underwater environments.
  • Acoustic fields are measured at vertically separated receivers.

Purpose of the Study:

  • To classify seabed types and estimate source ranges using acoustic data.
  • To improve signal processing for enhanced underwater acoustic analysis.

Main Methods:

  • Gaussian processes applied for data denoising and prediction at virtual receivers.
  • Machine learning used to map enhanced acoustic signals to sediment-range classes.
  • Analysis of acoustic fields across different environments and ranges.

Main Results:

  • Gaussian processes significantly improved denoising of acoustic data.
  • Enhanced acoustic fields led to superior classification accuracy.
  • Successful mapping of signals to 15 distinct sediment-range classes.

Conclusions:

  • Gaussian process-based denoising enhances acoustic data for seabed classification.
  • Machine learning combined with enhanced acoustic data offers a robust method for environmental analysis.