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Horizontal array passive inversion in range-independent environments

机译:与范围无关的环境中的水平阵列被动反演

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Survey techniques for geo-acoustic bottom characteristics are expensive and time consuming. The U.S. Navy has investigated several inversion techniques to characterize seabed sediments, most of which use active sonar and a vertical array. Passive techniques offer the potential to remain covert and extend the area of seabed characterization. This paper describes a passive approach, called Passive Geo-Acoustic Inversion Techniques (PGAIT) that uses matched-field processing on signals from ships of opportunity for water sound speed and sediment characterization. The signals are received on a horizontal array and broadband and temporal averaging techniques are used to reduce ambiguities. The algorithms are robust to environmental model mismatch and usually produce an output that is sufficient to identify sediment types. PGAIT performance is demonstrated at frequencies between 110 and 190 Hz in several sediment conditions, ranging from very soft to very hard and with several water mixed-layer depths. The results show that: 1) inversion can be effective on a horizontal array; 2) sediment properties are much easier to estimate than mixed-layer depths; and 3) incorrect assumptions about the assumed sound-speed profile do not significantly affect the results.
机译:地声底部特征的勘测技术既昂贵又费时。美国海军已经研究了几种反演技术来表征海底沉积物,其中大多数使用有源声纳和垂直阵列。无源技术提供了保持隐秘和扩展海底特征描述领域的潜力。本文介绍了一种称为“被动地声反演技术”(PGAIT)的被动方法,该方法对来自机会船的信号进行匹配场处理,以进行水声速和沉积物表征。在水平阵列上接收信号,并使用宽带和时间平均技术来减少歧义。该算法对环境模型不匹配具有鲁棒性,通常产生足以识别沉积物类型的输出。 PGAIT性能在几种沉积条件下(介于从非常软到非常坚硬以及具有几个水混合层深度的情况下)在110至190 Hz的频率下得到了证明。结果表明:1)反演对水平阵列有效。 2)与混合层深度相比,泥沙性质更容易估计; 3)关于假设声速分布的错误假设不会显着影响结果。

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