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马耳他高原联合反向散射和反射反演的海底粗糙度参数。

Seabed roughness parameters from joint backscatter and reflection inversion at the Malta Plateau.

机构信息

School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia V8W 3P6, Canada.

出版信息

J Acoust Soc Am. 2013 Sep;134(3):1833-42. doi: 10.1121/1.4817833.

Abstract

This paper presents estimates of seabed roughness and geoacoustic parameters and uncertainties on the Malta Plateau, Mediterranean Sea, by joint Bayesian inversion of mono-static backscatter and spherical wave reflection-coefficient data. The data are modeled using homogeneous fluid sediment layers overlying an elastic basement. The scattering model assumes a randomly rough water-sediment interface with a von Karman roughness power spectrum. Scattering and reflection data are inverted simultaneously using a population of interacting Markov chains to sample roughness and geoacoustic parameters as well as residual error parameters. Trans-dimensional sampling is applied to treat the number of sediment layers and the order (zeroth or first) of an autoregressive error model (to represent potential residual correlation) as unknowns. Results are considered in terms of marginal posterior probability profiles and distributions, which quantify the effective data information content to resolve scattering/geoacoustic structure. Results indicate well-defined scattering (roughness) parameters in good agreement with existing measurements, and a multi-layer sediment profile over a high-speed (elastic) basement, consistent with independent knowledge of sand layers over limestone.

摘要

本文通过对单站反向散射和球形波反射系数数据的联合贝叶斯反演,给出了地中海马耳他高原海底粗糙度和地声参数及其不确定性的估计。数据采用覆盖弹性基底的均匀流体沉积物层进行建模。散射模型假设具有 von Karman 粗糙度功率谱的随机粗糙水-沉积物界面。通过一组相互作用的马尔可夫链同时对散射和反射数据进行反演,以采样粗糙度和地声参数以及残差误差参数。跨维采样用于将沉积物层的数量和自回归误差模型的阶数(零阶或一阶)视为未知量。结果以边际后验概率分布和分布的形式呈现,这些分布和分布量化了有效数据信息量,以解决散射/地声结构问题。结果表明,粗糙度参数定义明确,与现有测量结果一致,并且在高速(弹性)基底上存在多层沉积物剖面,与石灰岩上存在砂层的独立知识一致。

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