Niu Haiqiang, Gong Zaixiao, Ozanich Emma, Gerstoft Peter, Wang Haibin, Li Zhenglin
State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing, 100190, People's Republic of China.
Scripps Institution of Oceanography, University of California San Diego, La Jolla, California 92093-0238, USA.
J Acoust Soc Am. 2019 Jul;146(1):211. doi: 10.1121/1.5116016.
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.
提出了一种基于大数据的深度学习方法，用于在海底参数不确定的海洋波导中使用单个水听器定位宽带声源。通过在由声学传播模型生成的大量声场副本上训练的几个50层残差神经网络，来处理源定位中的海底不确定性。提出了一种两步训练策略来改进深度模型的训练。首先，将距离在一个粗网格（5公里）中离散化。随后，在一个更精细的网格（0.1公里和2米）上对所选区间内的源距离和源深度进行离散化。针对不确定环境中的模拟仅幅度多频数据演示了深度学习方法。来自中国黄海的实验数据也验证了该方法。
