# Deep-learning source localization using multi-frequency magnitude-only data

## TL;DR

This paper introduces a deep learning method using residual neural networks to accurately locate broadband acoustic sources with magnitude-only data in ocean environments, effectively handling bottom uncertainty.

## Contribution

It presents a novel two-step training strategy and demonstrates the effectiveness of deep residual networks for source localization in uncertain ocean waveguides.

## Key findings

- Accurate source localization achieved with simulated data.
- Validated approach with real experimental data from the China Yellow Sea.
- Effective handling of bottom parameter uncertainties.

## Abstract

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models. First, the range is discretized in a coarse (5 km) grid. Subsequently, the source range within the selected interval and source depth are discretized on a finer (0.1 km and 2 m) grid. The deep learning methods were demonstrated for simulated magnitude-only multi-frequency data in uncertain environments. Experimental data from the China Yellow Sea also validated the approach.