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Related Concept Videos

Sound as Pressure Waves01:17

Sound as Pressure Waves

Sound waves, which are longitudinal waves, can be modeled as the displacement amplitude varying as a function of the spatial and temporal coordinates. As a column of the medium is displaced, its successive columns are also displaced. As the successive displacements differ relatively, a pressure difference with the surrounding pressure is created. The gauge pressure varies across the medium.
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Related Experiment Video

Updated: May 23, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Bayesian geoacoustic inversion using wind-driven ambient noise.

Jorge E Quijano1, Stan E Dosso, Jan Dettmer

  • 1School of Earth and Ocean Sciences, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P 5C2, Canada. jorgeq@uvic.ca

The Journal of the Acoustical Society of America
|April 17, 2012
PubMed
Summary
This summary is machine-generated.

This study uses Bayesian inversion and ambient noise to determine seabed properties. Wind speed significantly influences the accuracy of these geoacoustic parameter estimations.

Related Experiment Videos

Last Updated: May 23, 2026

Flying Insect Detection and Classification with Inexpensive Sensors
05:16

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

Area of Science:

  • Oceanography
  • Geophysics
  • Acoustics

Background:

  • Seabed geoacoustic parameter estimation is crucial for underwater acoustics.
  • Ambient noise measurements offer a passive method for characterizing the seafloor.
  • Previous methods often require active sound sources, limiting applicability.

Purpose of the Study:

  • To apply Bayesian inversion to wind-driven ambient noise data for seabed geoacoustic parameter quantification.
  • To assess the information content constraining these parameters using a vertical line array.
  • To investigate the influence of wind speed on the resolution of geoacoustic models.

Main Methods:

  • Utilized Bayesian inversion on bottom-loss data derived from ambient noise measurements.
  • Employed a ray-based forward model for fast bottom loss computation in a layered seabed.
  • Incorporated array finite aperture effects and wind speed as a driving mechanism for ambient noise.
  • Included a frequency-dependent signal-to-noise ratio in the inversion process.

Main Results:

  • Wind speed was found to significantly impact the resolution of seabed geoacoustic parameters.
  • Marginal probability distributions from Bayesian inversion quantified this resolution.
  • The method was successfully applied to simulated and experimental MAPEX 2000 data.
  • Results showed good agreement with active-source inversions and core measurements.

Conclusions:

  • Bayesian inversion of ambient noise data provides a viable method for seabed geoacoustic characterization.
  • Wind speed is a critical factor influencing the accuracy and resolution of these estimations.
  • The developed methodology offers a powerful tool for passive acoustic seafloor remote sensing.