论文标题
表面波基于中频范围内的远程声学反向散射的预测
Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range
论文作者
论文摘要
对于以前从未对海面探测应用进行过充分研究的频带获得了新数据。在描述的2周中,海洋实验1-3 kHz的音调脉冲是从位于北部黑海架上的平台上发出的,并研究了混响的多普勒光谱。我们认为,由于声音传播范围足够大,因此值得进一步研究,以满足沿海地区的实际需求,而角度距离分辨率则相当中等。但是,很难解释获得的数据,因为反向散射频谱形状受一系列效果的影响,并且与风波和电流参数有复杂的联系。在2海里约2海里的距离内收到了声学信号的反向散射。通过使用机器学习工具处理此类信号处理,估计了显着的波高,显性波频率。训练了基于决策树的数学回归模型来解决反问题。风波预测与直接测量,在平台上进行,机器学习结果可以进行物理解释。
New data was obtained for a frequency band that had not been so well-studied for sea surface probing applications before. During the described 2-weeks sea experiment 1-3 kHz tonal pulses were emitted from a platform, located on the northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We believe that this band is worth further studying due the sound propagation range is large enough to meet practical needs in coastal zone while the angle-distance resolution is quite moderate. However it is quite difficult to interpret the obtained data since backscattering spectrum shape is influenced by a series of effects and has a complicated link to wind waves and currents parameters. Backscattering of acoustical signals was received for distances around 2 nautical miles. Significant wave height, dominant wave frequency were estimated as the result of such signals processing 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.