An Automatic Internal Wave Recognition Algorithm Based on CNN Applicable to an Ocean Data Buoy System

Abstract

The application of internal wave recognition to the buoy system is of great significance to enhance the understanding of the ocean internal wave phenomenon and provide more accurate data and information support. This article proposes an automatic internal wave recognition algorithm based on convolutional neural networks (CNN), which is used in the tight-profile intelligent buoy system. The sea profile temperature data were collected using the Bailong buoy system in the Andaman Sea in 2018. The CNN network structure is applied to feature compression of ocean temperature profile data, reducing the input feature amount of the feature recognition network, thereby reducing the overall algorithm parameters and computational complexity. By adjusting the number of convolution kernels and the length of convolution steps, the original data features in the time domain and the space domain are compressed, respectively. The experimental results show that the identification accuracy and robustness of this method are clearly superior to those of other methods. Additionally, the parameter number and calculation amount of this algorithm are very tiny, which greatly improves the possibility of its deployment in the buoy system.

Keywords

• internal wave recognition
• buoy system
• convolutional neural networks
• model optimization
• feature extraction