基于注意力机制的全海深声速剖面预测方法

海水中的声速剖面具有明显的时间演化特性,其预测问题可以看作一个非线性的时间序列预测问题.解决此类问题的常用方法大多使用预定义的非线性形式,无法捕捉真正潜在的非线性关系.循环神经网络作为一种为序列建模特别设计的深度神经网络,在捕捉非线性关系上具有极大的灵活性,在非线性自回归的时间序列预测这一问题上展现了它的有效性;注意力...

Full-sea Depth Sound Speed Profiles Prediction Using RNN and Attention Mechanism

The Sound Speed Profiles (SSPs) in sea water have obvious time evolution characteristics, and their prediction can be regarded as a nonlinear time series prediction. Recurrent Neural Networks (RNN), a type of deep neural network designed for sequence modeling, can capture nonlinear relationships flexibly. Attention Mechanism (AM), which selects the most critical information for the current task, can describe the nonlinear relationships in space and temporal dimensions. In this paper, RNN and AM are used to construct a multivariate time series prediction model to learn the historical SSPs and predict the time-varying full-sea SSPs in shallow sea environment. Experiments on real sound speed data show that the proposed method is effective and outperforms other methods, and provides a new idea for the combination of physical model and machine learning in underwater acoustics.