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Modal wavenumber estimation by combining physical informed neural network.

Xiaolei Li1, Pengyu Wang2, Wenhua Song3

  • 1College of Marine Technology, Ocean University of China, Qingdao, 266100, China.

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|May 2, 2023
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Summary
This summary is machine-generated.

This study introduces a combining physical informed neural network (CPINN) for estimating modal wavenumbers in shallow water. CPINN accurately determines wavenumbers using vertical linear array data without needing source-receiver distance information.

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Area of Science:

  • Ocean Acoustics
  • Signal Processing
  • Machine Learning

Background:

  • Modal wavenumbers are crucial for understanding geoacoustic properties and matched field processing in shallow water waveguides.
  • Vertical Linear Arrays (VLAs) record sound fields that can be decomposed into modal depth functions, which are governed by differential equations related to modal wavenumbers.

Purpose of the Study:

  • To introduce a novel deep neural network, the combining physical informed neural network (CPINN), for accurate modal wavenumber estimation.
  • To evaluate the performance of CPINN in noisy shallow water environments using simulated data.

Main Methods:

  • CPINN utilizes proxies for modal depth functions and enforces physical constraints by ensuring these proxies satisfy the relevant differential equations.
  • The method employs data from a Vertical Linear Array (VLA) for modal wavenumber estimation.

Main Results:

  • CPINN successfully estimates modal wavenumbers without requiring knowledge of the horizontal distance between the sound source and the VLA.
  • The method demonstrates capability in estimating modal wavenumbers even when the source's range aperture is smaller than the maximum modal cycle distance.
  • CPINN performs effectively in range-dependent waveguides.

Conclusions:

  • CPINN offers a robust and flexible approach for modal wavenumber estimation in shallow water acoustics.
  • The developed method advances data-driven geoacoustic inversion and matched field processing techniques.