  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.
