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Related Concept Videos

Echo01:06

Echo

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The human ear cannot distinguish between two sources of sound if they happen to reach within a specific time interval, typically 0.1 seconds apart. More than this, and they are perceived as separate sources.
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Reflection of Waves01:07

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When a wave travels from one medium to another, it gets reflected at the boundary of the second medium. A common example of this is when a person yells at a distance from a cliff and hears the echo of their voice. The sound waves (longitudinal waves) traveling in the air are reflected from the bounding cliff. Similarly, flipping one end of a string whose other end is tied to a wall causes a pulse (transverse wave) to travel through the string, which gets reflected upon reaching the wall. In...
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Related Experiment Video

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The Frequency Domain Thermoreflectance Technique for Thermal Property Measurements
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Passive fathometer reflector identification with phase shift modeling.

Zoi-Heleni Michalopoulou1, Peter Gerstoft2

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

The Journal of the Acoustical Society of America
|August 1, 2016
PubMed
Summary
This summary is machine-generated.

Accurate seabed reflection analysis requires modeling wavelet phase shifts. This study demonstrates how accounting for phase shifts improves the identification and strength estimation of seabed reflectors using Bayesian methods.

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Area of Science:

  • Geophysics
  • Oceanography
  • Signal Processing

Background:

  • Passive fathometer processing uses wavelets in Green's function estimates to locate seabed reflectors and their properties.
  • Bayesian methods have been effective for identifying reflectors and layer interfaces.
  • Phase shifts in wavelets can impede accurate reflector identification.

Purpose of the Study:

  • To investigate the significance of modeling wavelet phase shifts in passive fathometer data.
  • To improve the accuracy of seabed reflector and property estimation.

Main Methods:

  • Utilized a Gibbs sampler for probabilistic computation of reflector depths, reflection strengths, and wavelet phase shifts.
  • Applied Bayesian inference to analyze passive fathometer data.

Main Results:

  • Demonstrated that incorporating phase shift modeling significantly enhances the estimation of reflector depths and strengths.
  • Showcased the ability of the Gibbs sampler to accurately compute probability densities for key parameters.

Conclusions:

  • Modeling wavelet phase shifts is crucial for accurate passive fathometer data interpretation.
  • Bayesian approaches, particularly with Gibbs sampling, provide a robust framework for addressing phase shift complexities in seabed analysis.