This study introduces a convolutional neural network based method for rapid prediction of underwater acoustic propagation fields, addressing the high computational cost of traditional methods. By analyzing regional terrain features and constructing a training dataset, the model learns acoustic transmission loss patterns across various terrain conditions. Tests in the Western Pacific demonstrate a root mean square error of 3.48 dB for non-smoothed fields, with an average prediction time of 1.95 ms per batch (10 samples). This method highlights the potential for fast acoustic propagation predictions using simplified inputs, offering a promising direction for real-time applications.
Wang et al. (Thu,) studied this question.