Quantifying Geoacoustic Uncertainty and Seabed Variability for Propagation Uncertainty

Abstract

LONG-TERM GOALS: Propagation and reverberation of acoustic fields in shallow waters depend strongly on the spatial variability of seabed geoacoustic parameters, and lack of knowledge of seabed variability is often a limiting factor in acoustic modeling applications. However, direct sampling (e.g., coring) of vertical and lateral variability is expensive and laborious, and matched-field and other long-range inversion methods fail to provide sufficient resolution. The long-term goal of this work is to use a Bayesian inversion approach in combination with seabed reflectivity data to investigate and quantify spatial variability of seabed sediments in two and three dimensions. For proper quantitative examination of spatial variability, it is important to differentiate between parameter estimate uncertainty, model parametrization effects, and actual spatial variability. To date, the project has developed an approach to quantify spatial variability of seabed sediments along a track (Dettmer et al. 2009ab) of point measurements separated by several kilometers. More recently advanced and general trans-dimensional inversion techniques (Dettmer et al. 2010ab 2011a) have been developed which provide more realistic estimates of environmental parameter uncertainties than previously possible in the acoustics community. In addition, Dettmer et al. (2011b) developed a trans-dimensional sequential Monte Carlo (SMC) algorithm to carry out seabed parameter inference on large data volumes along range-dependent tracks, providing two-dimensional (2D) geoacoustic uncertainty models of high vertical and lateral resolution. Further development of this methodology is an ongoing effort that will lead to rigorous 2D and three-dimensional (3D) geoacoustic uncertainty estimation from towed-array data in complex shallow-water environments (Holland et al. 2011).

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2011
Accession Number
ADA571585

Entities

People

  • Charles W. Holland
  • Jan Dettmer
  • Stan E. Dosso

Organizations

  • University of Victoria

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Acoustics
  • Algorithms
  • Ambient Noise
  • Autonomous Underwater Vehicles
  • Bayesian Inference
  • High Resolution
  • Inverse Problems
  • Monte Carlo Method
  • Particles
  • Physical Theories
  • Probability
  • Reflection
  • Shallow Water
  • Three Dimensional
  • Towed Arrays
  • Two Dimensional
  • Uncertainty

Fields of Study

  • Environmental science

Readers

  • Acoustical Oceanography.
  • Computational Modeling and Simulation

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference