浅海沉积物高频声波分类
High-Frequency Acoustic Sediment Classification in Shallow Water
摘要：
介绍了一种高频（12 kHz）多波束声纳数据的地声反演技术，作为浅水（40- 300 m）海底沉积物分类的一种手段。反演利用后向散射数据在各种掠射角估计平均粒度。需要沉积物类型和大量的多波束数据收集与海军海洋学办公室的Simrad EM 121 A系统，促进了算法的发展，以处理EM 121 A声学后向散射到沉积物类型的地图。采用APL-UW（华盛顿大学应用物理实验室）后向散射模型，结合模拟退火算法反演了6个地声参数。对于反演，根据与平均粒度的经验相关性，其中三个参数被约束，所述平均粒度被引入作为无约束参数。四个无约束（自由）参数是平均粒度，沉积物体积的相互作用，和两个海底粗糙度参数。声学沉积物分类是在Onslow湾地区的北卡罗莱纳州海岸使用12 kHz的Simrad EM 121 A多波束声纳系统的数据。原始水听器数据波束形成为122个波束，在底土上具有120度条带，并且计算每个波束和每个ping的后向散射强度。地面实况由声纳测量附近的68个抓斗样品组成，这些样品已经过平均粒度分析。反演得到的平均粒径与地面实测值的一致率为90%，可能是浅水区高频声学沉积物分类的有用工具。
英文摘要：
A geoacoustic inversion technique for high-frequency (12 kHz) multibeam sonar data is presented as a means to classify the seafloor sediment in shallow water (40--300 m). The inversion makes use of backscattered data at a variety of grazing angles to estimate mean grain size. The need for sediment type and the large amounts of multibeam data being collected with the Naval Oceanographic Office's Simrad EM 121A systems, have fostered the development of algorithms to process the EM 121A acoustic backscatter into maps of sediment type. The APL-UW (Applied Physics Laboratory at the University of Washington) backscattering model is used with simulated annealing to invert for six geoacoustic parameters. For the inversion, three of the parameters are constrained according to empirical correlations with mean grain size, which is introduced as an unconstrained parameter. The four unconstrained (free) parameters are mean grain size, sediment volume interaction, and two seafloor roughness parameters. Acoustic sediment classification is performed in the Onslow Bay region off the coast of North Carolina using data from the 12kHz Simrad EM 121A multibeam sonar system. Raw hydrophone data is beamformed into 122 beams with a 120-degree swath on the ocean floor, and backscattering strengths are calculated for each beam and for each ping. Ground truth consists of 68 grab samples in the immediate vicinity of the sonar survey, which have been analyzed for mean grain size. Mean grain size from the inversion shows 90\% agreement with the ground truth and may be a useful tool for high-frequency acoustic sediment classification in shallow water.
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