Feature Mining Method Based on Deep Learning for Underwater Electric Field Prediction in Marine Environment

The low-frequency electric field generated by various electromagnetic effects in the ocean overwrites the useful signal in amplitude and frequency, as a background distraction field for electric field detection. Effective electric field characteristic analysis and prediction methods are helpful to improve the environmental adaptability of the equipment and the ability of target detection. The deep learning method is used to mine the characteristics of underwater electric field in the marine environment to improve the prediction accuracy. In order to explore the relationship between seawater environmental parameters and electric field characteristics, parameters including measure time, seawater conductivity, water depth and wave height are used as independent variables, and the amplitude of the underwater electric field in the marine environment is used as the dependent variable. The relationship model between independent variables and dependent variables are established by deep neural network training to realize the prediction of underwater electric field characteristics in the marine environment. The results of 103 training samples and 2 test samples show that the method can effectively identify the test samples, and can be used for the mining and prediction of underwater electric field characteristics, as well as the detection and identification of weak electric field signals submerged in the marine environment.