[论文解读] Surface Waves Prediction Based on Long-Range Acoustic Backscattering in a Mid-Frequency Range

A. V. Ermoshkin, Dmitry A. Kosteev+3 more|arXiv (Cornell University)|May 25, 2022

Underwater Acoustics Research参考文献 37被引用 10

一句话总结

本研究展示了利用1–3 kHz频段的长距离声学后向散射预测有效波高和主波频率的可行性。通过分析黑海中频声呐实验中混响信号的多普勒谱，作者采用基于XGBoost的机器学习模型，利用物理特征训练，对海面波浪参数进行估计，预测结果与现场测量数据高度一致，标志着一种利用水下声学进行远程海洋表面监测的新方法。

ABSTRACT

Underwater acoustic echosounding for surface roughness parameters retrieval is studied in a frequency band that is relatively new for such purposes. During the described 2-weeks sea experiment, 1–3 kHz tonal pulses were emitted from an oceanographic platform, located on the northern Black Sea shelf. Doppler spectra of the resulting reverberation were studied. The frequency band of the acoustic system, selected for this study, is chosen due to the fact that the sound propagation range is large enough for remote sensing in a coastal zone, and the resolution cell size does not limit the research. Backscattering of acoustical signals was received for distances around two nautical miles. However, it turned to be quite difficult to interpret the obtained data since backscattering spectrum shape was influenced by a series of effects, resulting in a complicated link to wind waves and currents’ parameters. Significant wave height and dominant wave frequency were estimated as the result of such signals processed with the use of machine learning tools. A decision-tree-based mathematical regression model was trained to solve the inverse problem. Wind waves prediction is in a good agreement with direct measurements, made on the platform, and machine learning results allow physical interpretation.

研究动机与目标

• 研究中频（1–3 kHz）水下声学后向散射在远程感测海面波浪参数方面的潜力。
• 解决从长距离混响信号反演有效波高和主波频率的逆问题。
• 评估机器学习在解释受多种物理散射机制影响的复杂声学多普勒谱方面的有效性。
• 通过14天海上实验期间收集的直接现场测量数据，验证模型预测结果。

提出的方法

• 连续声呐系统从黑海北部陆架平台发射1–3 kHz的纯音脉冲。
• 在约2海里的距离上记录了后向散射混响信号的多普勒谱。
• 从多普勒谱中提取了物理特征（如布拉格散射强度、谱偏度和时间延迟），作为机器学习的输入。
• 基于XGBoost的回归模型被训练，以根据声学特征预测有效波高和主波频率。
• 进行了特征重要性分析，以识别预测模型中最具影响力的物理参数。
• 利用本地现场测量的风速、波高和海流速度数据对模型进行校准。

实验结果

研究问题

• RQ1 1–3 kHz频段的长距离声学后向散射能否用于估算有效波高和主波频率？
• RQ2 多普勒后向散射谱的物理特征与实际海面波浪参数之间有何相关性？
• RQ3 像XGBoost这样的机器学习模型在从复杂且噪声较大的声学混响数据中准确重构波浪参数方面，其性能如何？
• RQ4 气泡散射和布拉格共振在中频下如何影响观测到的多普勒谱形态？
• RQ5 模型性能在多大程度上依赖于基于散射机制物理理解的特征选择？

主要发现

• XGBoost模型在预测的有效波高和主波频率与现场实测值之间表现出良好一致性，验证了该方法在远程波浪监测中的可行性。
• 对预测最重要的特征被识别为谱偏度（sk_le）和布拉格散射强度（lv_brag_le），尤其在特定角度和频率下表现突出。
• 来自时间延迟较小（如0–30 ms）信号的谱特征在估计坐标系中心区域的海面状态时被优先考虑。
• 模型性能对时间同步高度敏感；时间对齐错误会导致无意义结果，凸显了精确数据对齐的重要性。
• 尽管风力较强，但未观察到散射强度显著增加，表明在所研究条件下，气泡散射在1–3 kHz频段可能并非主导机制。
• 在中等至恶劣海况下，谱中未出现明显的布拉格峰，表明布拉格共振无法单独解释全部散射行为，其他机制可能也起作用。