This paper presents a sequential approach to infer sediment geoacoustic properties from the observation of vertical specific acoustic impedance due to ship noise. This acoustic quantity does not require knowledge of the source and is sensitive to ocean bottom properties including density. The approach is demonstrated for the characterization of sediment off the small Senegalese coast during EHL-IRD joint experiments [ECOAO 13]. The noise field due R/V Antea sailing parallel to the coast was recorded on a vertical, multi-wavelength pressure-gradient array (EHL) from which impedance data was derived. A particle filter (PF) simultaneously tracks the range variations of impedance at a number of discrete frequencies in order to output a sequence of environmental parameter estimates with their associated uncertainties in the form of posterior probability densities (PPDs). The range-averaged inversion results are in good agreement with those produced by a classical batch inversion method based on a genetic algorithm (GA). Apparent inhomogeneity of the ocean bottom is observed, which is consistent with the sieving analysis of sediment grab samples collected at two different locations. When compared to batch processing, the computational efficiency and robustness of particle filtering are due to the capacity of iteratively updating the estimated PPDs, as is demonstrated by implementing the inversion with different particle sizes, of 200, 300 and 400.
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机译:本文提出了一种连续的方法来推断垂直声阻抗的观察沉淀地声学特性由于噪声出货。这声学量不需要源的知识和对海洋底部性质,包括密度敏感。该方法是证明了沉积物的断小型塞内加尔海岸期间EHL-IRD联合实验[ECOAO 13]的表征。由于平行于海岸R / V ANTEA航行噪声场记录的垂直,多波长压力梯度阵列(EHL),从该阻抗数据推导上。的颗粒过滤器(PF)同时跟踪阻抗的在多个离散的频率范围的变化,以输出环境参数估计的序列,在后验概率密度(PPD的)形式及其相关联的不确定性。的范围内平均的反演结果与那些通过基于遗传算法(GA)一个经典的分批反转方法产生了良好的一致。海洋底部的不均匀性明显观察到,这与在两个不同的位置收集沉积物抓取样品的筛分分析结果一致。当与批量处理,计算效率和粒子滤波的鲁棒性是由于迭代地更新所述估计的PPD的能力,如通过实施的200,300和400具有不同粒度的反演,证明。
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