发表机构
Faculty of Information Science and Engineering, Ocean University of China; School of Space Science and Technology, Shandong University at Weihai; School of Artificial Intelligence, The Chinese University of Hong Kong (Shenzhen)（中国海洋大学信息科学与工程学院; 山东大学威海校区空间科学与技术学院; 香港中文大学（深圳）人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对传统模型难以泛化多尺度声速分布的问题，本文提出轻量级混合基函数增强Kolmogorov-Arnold网络（LHBF-KAN），通过多分支表示层适配多尺度演化，结合剪枝策略实现紧凑架构，适用于水下平台部署。
AI 中文摘要
水下声速分布直接决定了声传播路径，对水下声学通信和目标定位至关重要。传统声速剖面（SSP）预测方法无需现场数据测量即可估算水下声速分布，突破了声呐观测设备的覆盖范围限制，使模型在大多数海域具有通用性。然而，水下声速存在多尺度变化，如内波等海洋过程引发的日变化、季度变化及间歇性波动，这使得现有方法中的固定结构模型难以对多尺度声速分布模式具备良好的泛化能力。为解决该问题，本文提出一种用于多尺度声速预测的轻量级混合基函数增强Kolmogorov-Arnold网络（LHBF-KAN）模型。我们旨在构建多分支表示层，其中不同基函数对应不同时间模式，从缓慢变化的背景趋势到动态海洋过程引发的快速波动，使模型能自然适配不同深度声速固有的多尺度演化。为防止多分支结构增大模型规模，进一步引入剪枝策略以抑制训练期间贡献持续较低的分支，从而得到紧凑架构，适用于资源受限的水下平台部署。
英文摘要
The underwater sound speed distribution directly governs acoustic propagation paths, rendering it critically important for underwater acoustic communication and target localization. Conventional sound speed profile (SSP) prediction methods provide a good way to estimate the underwater sound speed distribution without on-site data measurement, thus breaking through the coverage area constraints of sonar observation equipment and making the model universal in most marine areas. However, underwater sound speed exhibits multi-scale variations, such as diurnal, quarterly, and intermittent fluctuations caused by ocean processes such as internal waves. This makes it difficult for the fixed structure models in existing methods to have good generalization ability for multi-scale sound speed distribution patterns. To tackle this problem, we proposed a lightweight hybrid basis-function empowered Kolmogorov-Arnold network (LHBF-KAN) model for multi-scale sound speed prediction. We aim to construct a multi-branch representation layer in which different basis functions respond to distinct temporal patterns, from slowly varying background trends to rapid fluctuations induced by dynamic ocean processes, allowing the model to naturally accommodate the inherently multi-scale evolution of sound speed at different depths. To prevent the multi-branch structure from increasing model size, a pruning strategy is further introduced to suppress branches with consistently low contribution during training, yielding a compact architecture, suitable for deployment on resource constrained underwater platforms.
