Unveiling the spatial-temporal dynamics: Diffusion-based learning of conditional distribution for range-dependent...

Area of Science:

• Oceanography
• Acoustics
• Machine Learning

Background:

• Precise forecasting of the sound speed field (SSF) is crucial for environment-aware underwater acoustic detection and communications.
• Current machine learning models show improvement over classical methods but have limitations in fully learning conditional distributions of future SSFs.

Purpose of the Study:

• To leverage diffusion models for enhanced conditional distribution learning of SSFs.
• To improve the accuracy of temporal and spatial SSF forecasting, especially under limited training data.

Main Methods:

• Utilized diffusion models, inspired by deep generative models like DALL-E 2 and SORA.
• Designed specific neural architectures and training strategies for conditional distribution learning.
• Conducted experiments using real-life datasets from the South China Sea.

Main Results:

• The proposed diffusion model outperforms state-of-the-art baselines in forecasting range-dependent SSFs.
• Accurate prediction of associated underwater transmission losses was achieved.
• The model demonstrated reliable confidence intervals for quantifying prediction uncertainties.

Conclusions:

• Diffusion models offer a powerful approach for learning conditional SSF distributions.
• This method significantly advances the capability for precise underwater acoustic environment prediction.
• The findings support improved reliability in underwater acoustic detection and communication systems.