GEOACOUSTIC INVERSION INSPIRED BY MACHINE LEARNING

Abstract

Our objectives include the development of new geoacoustic inversion methods, their use in the analysis of shallow water experimenta""l data, and evaluation of geoacoustic model parameter uncertainties including the mapping of these uncertainties through to system p"erformance uncertainties. Of specific technical interest are the development of methods to estimate and track environmental paramete"rs using: (1) sparse sampling, (2) machine learning, and (3) ambient noise. These methods will be demonstrated using data collected"" during the SeabedCharacterization Experiment 2017.

Document Details

Document Type
DoD Grant Award
Publication Date
Feb 20, 2018
Source ID
N000141812118

Entities

People

  • Peter Gerstoft

Organizations

  • Office of Naval Research
  • United States Navy
  • University of California, San Diego

Tags

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks