Abstract
The sound speed profile (SSP) is a core environmental parameter for underwater acoustic detection, navigation, communication, and other applications. However, its accurate acquisition is constrained by the sparsity of observational data and the ill-posed nature of inversion problems. This paper systematically reviews the research progress of SSP inversion under sparse observation constraints. The review traces the technical evolution from early physical models to current intelligent paradigms, classifies and compares mainstream inversion methods, presents typical application scenarios with quantitative case studies, provides a comparison of all kinds of SSP acquisition routes, and discusses critical challenges and future trends. The review reveals that current AI-driven methods achieve a practical accuracy of approximately 1–2 m/s but face bottlenecks in interpretability, cross-regional generalization, and extreme-condition robustness. Fusing physical constraints with multi-source sparse data (remote sensing, in-situ discrete measurements) emerges as the core direction for balancing inversion accuracy, efficiency, and cost. This paper provides a comprehensive reference for technical selection in marine acoustics, ocean observation, and underwater operations.