A Spatially Informed Machine Learning Method for Predicting Sound Field Uncertainty

Predicting the uncertain distribution of underwater acoustic fields, influenced by dynamic oceanic parameters, is critical for acoustic applications that rely on sound field characteristics to generate predictions. Traditional methods, such as the Monte Carlo method, are computationally intensive and thus unsuitable for applications requiring high real-time performance and flexibility. Current machine learning methods excel at improving computational efficiency but face limitations in predictive performance, especially in shadow areas. In response, a machine learning method is proposed in this paper that balances accuracy and efficiency for predicting uncertainties in deep ocean acoustics by decoupling the scene representation into two components: (a) a local radiance model related to environmental factors, and (b) a global representation of the overall scene context. Specifically, the internal relationships within the local radiance are first exploited, aiming to capture fine-grained details within the acoustic field. Subsequently, local clues are combined with receiver location information for joint learning. To verify the effectiveness of the proposed approach, a dataset of historical oceanographic data has been compiled. Extensive experiments validate the efficiency compared to traditional Monte Carlo techniques and the superior accuracy compared to existing learning method.