Acoustic field prediction in Pekeris waveguides using a learnable effective depth.

Authors

Zeyu Wang, Yongxian Wang, Yanqun Wu, Zhao Sun, Yunxiang Zhang, Houwang Tu

Published in

JASA express letters. Volume 6. Issue 10. Oct 01, 2026.

Abstract

Predicting acoustic fields from sparse measurements using physics-informed neural networks (PINNs) is challenging under uncertain bottom geoacoustic parameters. This paper proposes a framework incorporating a learnable effective-depth bottom boundary and two-stage training strategy, called LED-PINN. For 1 km range extrapolation beyond a 3 km measurement interval (ratio 1/3), LED-PINN reduces the mean absolute error from 7.42 dB for OceanPINN, a PINN-based model for ocean acoustic field prediction, to 2.41 dB, while improving depth extrapolation and keeping the effective-depth inversion error below 0.6 m. These results demonstrate improved acoustic field extrapolation under uncertain bottom parameters.

PMID:
42895449
Bibliographic data and abstract were imported from PubMed on 19 Oct 2026.

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