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Updated: May 24, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
07:46

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Published on: April 20, 2016

Sensitivity analysis of normal mode algorithms using automatic differentiation.

Ariel Vardi1,2, Gil Averbuch1, John J Leonard2

  • 1Department of Applied Ocean Physics and Engineering, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts 02543, USA.

The Journal of the Acoustical Society of America
|May 22, 2026
PubMed
Summary

This study introduces a differentiable KRAKEN normal mode model for underwater acoustics. It enables precise sensitivity analysis of sound speed variations, aiding in geoacoustic inversion and source localization.

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Area of Science:

  • Ocean acoustics
  • Geophysics
  • Computational modeling

Background:

  • Underwater acoustic propagation is influenced by sound speed variations.
  • Accurate modeling is crucial for understanding sound propagation and performing inversions.
  • Existing models may lack the sensitivity information needed for advanced inversion techniques.

Purpose of the Study:

  • To develop a differentiable normal mode model for underwater acoustics.
  • To enable gradient-based inversion methods by calculating precise sensitivities.
  • To provide a foundation for enhanced geoacoustic inversion and source localization.

Main Methods:

  • Implemented automatic differentiation on the KRAKEN normal mode model's eigenvalue problem.
  • Calculated sensitivities of modal quantities to geoacoustic parameters and frequency.
  • Performed sensitivity analyses on benchmark problems.

Main Results:

  • Achieved machine-precision accuracy for sensitivities.
  • Demonstrated the model's utility through sensitivity analyses.
  • Identified parameter significance for inversion.

Conclusions:

  • The differentiable KRAKEN model facilitates gradient-based inversion.
  • Accurate sensitivity calculations are vital for underwater acoustic applications.
  • This work lays the groundwork for more efficient inversion techniques.