Work

Source localization in shallow ocean using a deep learning approach with range-dependent sound speed profile modeling

Year: 2025

Type: article

Abstract: Model-based deep learning approaches provide an alternative scheme to address the problem of the shortage of training data. However, performance degradation caused by sound speed profile (SSP) mismatch remains a critical challenge, particularly in shallow-water environments influenced by internal waves. In this paper, a simple range-dependent SSP model is integrated into the deep learning approach for source localization. The network trained on simulated data generated with the range-dependent SSP model performs well on validation data and generalizes to experimental test data after transfer learning with limited experimental samples.

Source: JASA Express Letters

DOI: 10.1121/10.0038764

Authors: Jing Guo, Juan Zeng

Institutions: Chinese Academy of Sciences, Institute of Acoustics, University of Chinese Academy of Sciences

Language: English

Topic: Underwater Acoustics Research

Subfield: Oceanography

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4413050854

FWCI: 0.496

Cites: 15

Cited by: 1

Related to: 10