Bayesian Ambient Noise Inversion for Geoacoustic Uncertainty Estimation

Abstract

Estimation of seabed geoacoustic parameters in shallow water by acoustic remote sensing remains a challenging task due to constraints on hardware, data collection and analysis, and cost of maritime surveys. This work focuses on the application of two techniques that might offer a solution to those constraints: the use of ambient noise to probe the seabed, and Bayesian inversion of these data to estimate geoacoustic parameters of interest together with their uncertainties. The long-term goal of this work is to establish general methods for processing and inverting ambient noise data and assessing the quality of the results by quantifying their uncertainties.

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Document Details

Document Type
Technical Report
Publication Date
Sep 30, 2013
Accession Number
ADA598823

Entities

People

  • Jan Dettmer
  • Jorge E. Quijano
  • Lisa Zurk
  • Martin Siderius
  • Stan E. Dosso

Organizations

  • Portland State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Agreements
  • Algorithms
  • Ambient Noise
  • Arrays
  • Bottom Loss
  • Computational Science
  • Grazing Angles
  • Inversion
  • Losses
  • Models
  • Monte Carlo Method
  • Noise
  • Probability
  • Sampling
  • Shallow Water
  • Signal Processing
  • Uncertainty

Readers

  • Acoustical Oceanography.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms