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Observation of stable components of the sound field in Lake Kinneret using the autoproduct transform

A. L. Virovlyansky

Abstract

An analysis was conducted of broadband sound pulses received by a vertical array in Lake Kinneret (Israel). For most frequencies within the pulse frequency bands, the array is sparse. The application of the autoproduct transform made it possible to approximately reconstruct the signals that would be received after the emission of pulses at low frequencies for which the array is dense. Using the coherent state method developed in quantum theory, a transition has been made from representing the reconstructed field as a function of depth and time to its distribution in the 'depth-angle-time' phase space. Due to the absence of multipath, the intensity distribution in this space should be weakly sensitive to variations in environmental parameters. In accordance with this expectation, the distribution found is close to the result of its calculation using an idealized (range-independent) waveguide model. It has been shown that this intensity distribution can be used as input data for a neural network when solving the problem of sound source localization in an underwater waveguide. In the examples considered, the neural network is trained on synthetic data, i.e., data obtained from theoretical calculations.

Observation of stable components of the sound field in Lake Kinneret using the autoproduct transform

Abstract

An analysis was conducted of broadband sound pulses received by a vertical array in Lake Kinneret (Israel). For most frequencies within the pulse frequency bands, the array is sparse. The application of the autoproduct transform made it possible to approximately reconstruct the signals that would be received after the emission of pulses at low frequencies for which the array is dense. Using the coherent state method developed in quantum theory, a transition has been made from representing the reconstructed field as a function of depth and time to its distribution in the 'depth-angle-time' phase space. Due to the absence of multipath, the intensity distribution in this space should be weakly sensitive to variations in environmental parameters. In accordance with this expectation, the distribution found is close to the result of its calculation using an idealized (range-independent) waveguide model. It has been shown that this intensity distribution can be used as input data for a neural network when solving the problem of sound source localization in an underwater waveguide. In the examples considered, the neural network is trained on synthetic data, i.e., data obtained from theoretical calculations.
Paper Structure (7 sections, 12 equations, 10 figures, 2 tables)

This paper contains 7 sections, 12 equations, 10 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Experimental data and environmental model
  3. Theory
  4. Autoproduct
  5. Theory vs experiment
  6. Source localization
  7. Conclusion

Figures (10)

  • Figure 1: Sound speed profiles and hydrophone positions in 2019 (left panel) and 2021 (right panel) (after V2023).
  • Figure 2: Signal amplitudes $|v(z, t)|$ on receiving array elements recorded from distances of 380 m (upper panel) and 905 m (lower panel). The white broken lines represent the time fronts.
  • Figure 3: Fragments of ray lines at distances of 380 m (upper panel) and 905 m (lower panel) corresponding to early arrival times.
  • Figure 4: Cross section of the intensity distribution $J(z, p, t)$ at a distance of 380 m by the plane $z = 20$ m. White circles indicate the intersection points of the cross-section plane and the ray line. Top and middle panels: Numerical simulations. Bottom panel: Experiment.
  • Figure 5: The same as in Fig. 4, but for a section by plane $t = 0.036$ s.
  • ...and 5 more figures