Data and Physics Co-Driven Prediction Method for Acoustic Field Uncertainty
Abstract
High-precision acoustic propagation field prediction plays a crucial role in supporting decision-making for underwater sonar systems. Neural networks offer advantages in acoustic field forecasting due to their ability to adaptively adjust weights based on real experimental data. Simulation results have demonstrated that integrating physical computation into the forward propagation of neural networks can significantly improve prediction accuracy. Building on this foundation, the present study further introduces a terrain feature clustering module to mitigate abrupt changes in the predicted acoustic field caused by sharp variations in topographic features, thereby enhancing the stability of the training process. Additionally, this study addresses acoustic field prediction under uncertainty in sound speed profiles (SSPs). An empirical orthogonal function is employed to extract features from SSPs, which are then incorporated as input to the neural network model. Expanding upon prior work, a dedicated analysis module for SSP features is introduced, and a staged training strategy is adopted for model optimization. Furthermore, the statistical uncertainty in acoustic field predictions is examined. Finally, model training and testing are further refined using experimental data collected from the South China Sea. Experimental results indicate that the inclusion of the terrain feature clustering module improves the average prediction accuracy of the final acoustic field by 0.3 dB. Compared with experimental data, the proposed neural network model achieves an accuracy improvement of 1.78 dB over numerical solutions based on coupled normal modes.