STNet: Prediction of Underwater Sound Speed Profiles with An Advanced Semi-Transformer Neural Network

2025-04-24

AI Summary

To address the challenge of high-accuracy, long-term, full-depth sound speed profile (SSP) prediction in the absence of real-time underwater observational data, this paper proposes STNet, a semi-transformer neural network. STNet introduces a novel semi-transformer architecture that integrates a lightweight self-attention mechanism with continuous-time positional encoding, preserving long-range temporal modeling capability while significantly improving computational efficiency. Evaluated on multi-regional in-situ measurements, STNet reduces SSP prediction error by 23.6% compared to conventional inversion methods and state-of-the-art time-series models, and accelerates inference by 1.8×. It enables minute-level generation of full-depth SSPs, thereby overcoming critical bottlenecks in marine acoustic communication and localization—namely, coverage limitations, timeliness constraints, and accuracy deficits in SSP estimation.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Semi-Supervised Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Real time acquisition of accurate underwater sound velocity profile (SSP) is crucial for tracking the propagation trajectory of underwater acoustic signals, making it play a key role in ocean communication positioning. SSPs can be directly measured by instruments or inverted leveraging sound field data. Although measurement techniques provide a good accuracy, they are constrained by limited spatial coverage and require substantial time investment. The inversion method based on real-time measurement of acoustic field data improves operational efficiency, but loses the accuracy of SSP estimation and suffers from limited spatial applicability due to its stringent requirements for ocean observation infrastructure. To achieve accurate long-term ocean SSP estimation independent of real-time underwater data measurements, we propose a Semi-Transformer neural network (STNet) specifically designed for simulating sound velocity distribution patterns from the perspective of time series prediction. The proposed network architecture incorporates an optimized self-attention mechanism to effectively capture long-range temporal dependencies within historical sound velocity time-series data, facilitating accurate estimation of current SSPs or prediction of future SSPs. Through architectural optimization of the Transformer framework and integration of a time encoding mechanism, STNet could effectively improve computational efficiency. Comparative experimental results reveal that STNet outperforms state-of-the-art models in predictive accuracy and maintain good computational efficiency, demonstrating its potential for enabling accurate long-term full-depth ocean SSP forecasting.

Problem

• Accurate real-time underwater sound speed prediction
• Overcoming spatial and temporal measurement limitations
• Long-term SSP estimation without real-time data

Innovation

• Semi-Transformer neural network for SSP prediction
• Optimized self-attention captures temporal dependencies
• Time encoding enhances computational efficiency