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Based on the data of the sound speed and the physical–mechanical properties of 300 seafloor sediment samples collected in the middle area of the Southern Yellow Sea, a backpropagation (BP) neural network algorithm is used herein to study the neural network topology and the corresponding neuron characteristic factors. A neural network model for predicting the sound speed of the seafloor sediment based on density, water content, porosity, void ratio, liquid limit, plastic limit, and clay content in the middle area of the Southern Yellow Sea is established. The results show that the sound speed prediction accuracy is the highest when the network model contains seven input layer neurons, ten hidden layer neurons, and one output node. According to the prediction results, the MAE(Mean Absolute Error) and the MAPE(Mean Absolute Percentage Error) are 13.19 m s −1  and 0.85%, respectively. The prediction results illustrate that the established BP neural network prediction method for the seafloor sediment sound speed is better than the traditional single, two and multi-parameter prediction equations.

Predicting the sound speed of seafloor sediments in the Middle area of the Southern Yellow Sea based on a BP neural network model