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

arXiv: https://arxiv.org/abs/1903.12319 · HTML: https://mlsanity.com/paper/1903.12319/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2019.

## Built on by (most cited first, 3 of 3)

- [Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach](https://mlsanity.com/paper/2207.10222/) (2022)
- [A physically-informed Deep-Learning approach for locating sources in a waveguide](https://mlsanity.com/paper/2208.04938/) (2022)
- [Joint Source-Environment Adaptation of Data-Driven Underwater Acoustic Source Ranging Based on Model Uncertainty](https://mlsanity.com/paper/2503.23258/) (2025)

## Abstract (arXiv)

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.