水下声速剖面构建方法研究进展

Research progress on methods for constructing underwater sound speed profile

摘要:

作为决定水声信号传播模式的重要因素之一,声速剖面反映了海水声速自海表至海底的变化,对其准确实时地构建水下信息系统的建设至关重要。面向水声探测领域,分析并总结了水下声速剖面构建方法的研究进展。根据支撑数据源的不同,主流的方法可以分为直接测量法、统计回归重构法以及声学反演法。在直接测量法方面,介绍了仪器直接测量法以及参数测量计算法;在统计回归重构法方面,总结了以经验正交函数回归为核心的重构框架以及结合神经网络的优化算法;在声学反演法方面,讨论了基于声场观测数据的匹配场处理及深度学习框架的性能。传统的直接测量法精度最高,但大范围应用的成本较高;统计回归重构法提高了便捷性,但依赖于数据库的数据质量;声学反演法尽管可解释性很强,但难以应用于声呐系统无法覆盖的区域。未来的水下声速剖面构建应着眼于智能化、精细化、实时化, 可为复杂海洋背景下的信息系统建设提供满足多层次需求的声速剖面构建结果。

Abstract:

As one of the important factors determining the propagation mode of underwater acoustic signals, the sound speed profile reflects the changes in seawater sound speed from the sea surface to the seabed. It is crucial for the accurate and real-time construction of underwa ter information systems. In the field of underwater acoustic detection, this paper analyzed and summarized the research progress on methods for constructing underwater sound speed pro file. According to the different supporting data sources, mainstream methods can be divided into direct measurement, statistical-regression reconstruction and acoustic inversion. In terms of direct measurement methods, this paper introduced the instrument direct measure ment method and the parameter measurement calculation method. In terms of statistical-re gression reconstruction methods, it summarized the reconstruction framework centered on empirical orthogonal function regression and the optimization algorithm combined with neural networks. In terms of acoustic inversion methods, it discussed the performance of matched field processing and deep learning frameworks based on sound field observation data. The tra ditional direct measurement method has the highest accuracy, but its cost for large-scale ap plication is relatively high; the statistical-regression reconstruction method improves conven ience, but it relies on the data quality of the database; although the acoustic inversion method has strong interpre-tability, it is difficult to apply to areas that sonar systems cannot cover. Fu ture construction of underwater sound speed profile should focus on intelligence, refinement, and real-time capabilities. This can provide results for the construction of information systems in com plex marine environments that meet multi-level requirements for sound speed profile construction.