This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content.

中文翻译：

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新英格兰泥块上模态色散数据的跨维反演

本文介绍了 2017 年新英格兰泥滩海底特征实验期间记录的两个独立数据集的单接收器地声反演。试验区水深约70 m，海床上层为含粘土（即泥）的细粒沉积物。本文考虑的第一个数据集是燃烧声源信号，第二个数据集是 J15 源发出的啁啾声。这两个数据集提供了关于海床地声特性的不同信息，这是由于它们不同的频率内容和环境的分散特性。对于这两个数据集，源/接收器范围约为 7 公里，模态时频频散曲线是使用扭曲估计的。然后将估计的离散曲线用作贝叶斯跨维反演算法的输入数据。因此可以从数据中推断出底层分层和地声参数（声速和密度）。本文强调了泥浆的重要特性，与独立的原位测量一致。它还演示了在相互轨道上收集的两个数据集的信息内容如何不同，但具有不同的声源和模态内容。