Ballard Megan S, Frisk George V, Becker Kyle M
Applied Research Laboratories, The University of Texas at Austin, Austin, Texas 78713.
Department of Ocean and Mechanical Engineering, Florida Atlantic University, Dania Beach, Florida 33004.
J Acoust Soc Am. 2014 Jun;135(6):3316-26. doi: 10.1121/1.4875715.
Estimates of the spatial and temporal variability of ocean sound speed on the New Jersey shelf were obtained using acoustic signals measured by a set of freely drifting buoys. The range- and time-dependent inversion problem is computationally intensive and a linearized perturbative algorithm was applied to obtain results in an efficient manner. The inversion algorithm uses estimates of modal travel time to determine sound speed as a function of range and depth. In order to handle the high volume of data associated with the acoustic sensing network, the modal travel time estimation process was automated using an adaptive time-frequency signal processing method known as time-warping. Time-warping is a model-based transform that converts the frequency-dependent modal arrivals to monotones in the warped domain where they can be easily filtered. The data analyzed in this paper were collected on 16 March 2011 on the New Jersey shelf when the ocean was relatively well-mixed. While the observed sound-speed variations are small, both spatial and temporal trends are observed in the results. Furthermore, the estimated sound-speed profiles show good agreement with temporally and spatially collocated measurements.
利用一组自由漂流浮标测量的声学信号,获得了新泽西海岸架海洋声速的空间和时间变异性估计值。距离和时间相关的反演问题计算量很大,因此应用了线性化微扰算法以高效地获得结果。反演算法使用模态传播时间估计值来确定作为距离和深度函数的声速。为了处理与声学传感网络相关的大量数据,使用一种称为时间规整的自适应时频信号处理方法对模态传播时间估计过程进行了自动化处理。时间规整是一种基于模型的变换,它将频率相关的模态到达信号转换为在规整域中的单调信号,在该域中它们可以很容易地被滤波。本文分析的数据于2011年3月16日在新泽西海岸架收集,当时海洋混合相对良好。虽然观测到的声速变化很小,但结果中同时观察到了空间和时间趋势。此外,估计的声速剖面与时间和空间上并置的测量结果显示出良好的一致性。