Physics-Informed Neural Networks for Ocean Acoustic Field Prediction with Envelope Smoothing

Predicting ocean acoustic fields in shallow water is challenging due to high spatial variability, with depth scales of 100 m and range scales of 1 km. Limited acoustic data further complicates this task. We propose a physics-informed neural network (PINN) with the Helmholtz equation as a physics constraint, enhancing prediction accuracy with scarce data. A preprocessing step using an envelope smoothing technique is introduced. This reduces the spatial field variability, enabling more accurate training of the PINN than purely data-driven approaches. Our method is validated through ocean data, demonstrating substantial improvements in PINN performance for complex ocean acoustic predictions.