Geoacoustic inversion

地声反演：海底地声参数，如沉积物分层结构、声速、密度、衰减和散射强度，强烈影响海洋声场的传播。因此，需要了解这些参数来理解和模拟浅水环境中的声音传播，这是许多声纳应用（以及岩土和地质目的）所需要的。通过海洋声学测量估计海底参数为直接采样（例如取芯）提供了一种经济且方便的原位替代方案，其灵敏度与声纳相关。然而，这需要解决本质上非唯一的强非线性反问题（即，一系列解可以可接受地拟合数据）。各种地声反演方法已得到应用，并取得了不同程度的成功，包括全场声学测量、声混响、海底反射率、环境噪声和船舶噪声的反演。这些方法的不同之处在于它们估计各种地声参数以及解析参数变化作为深度和/或范围的函数的能力；然而，这些方法的有效性尚未得到系统或严格的检验。本提案的总体目标是开发、定量研究和比较上述地声反演方法。地声反演定量分析的一个关键组成部分是估计恢复参数的不确定性，它表明了数据的信息含量，为不同数据集的比较提供了客观依据。此外，只有通过定量的不确定性估计，才能以有意义的方式解决诸如解决参数空间变异性或频率依赖性等问题（例如，区分不确定性与变异性）。非线性反演中的参数不确定性估计本身就是一个具有挑战性的研究问题。该提案基于贝叶斯推理公式和正在开发的数值（统计）采样方法，该方法根据数据和可用的物理约束（先验信息）建立未知海底参数的概率分布。

GEOACOUSTIC INVERSION: Seabed geoacoustic parameters, such as sediment layering structure, sound speed, density, attenuation and scattering strength, strongly affect the propogagion of ocean acoustic fields. Hence, knowledge of these parameters is required to understand and model sound propagation in shallow-water environments, as required for many sonar applications (as well as geotechnical and geological purposes). Estimating seabed parameters from ocean acoustic measurements provides an economical and convenient in situ alternative to direct sampling (e.g., coring), with a sensitivity relevant to sonar. However, this requires solving a strongly nonlinear inverse problem that is inherently nonunique (i.e., a range of solutions can acceptably fit the data). A variety of geoacoustic inversion approaches have been applied, with varying degrees of success, including the inversion of full-field acoustic measurements, acoustic reverberation, seabed reflectivity, ambient noise, and ship noise. These approaches differ in their ability to estimate various geoacoustic parameters and to resolve parameter variations as a function of depth and/or range; however, the efficacy of these approaches has not been examined in a systematic or rigorous way. The overall objectives of this proposal are to develop and quantitatively study and compare the above-mentioned approaches to geoacoustic inversion. A key component of quantitative analysis of geoacoustic inversion is estimating the uncertainties of the recovered parameters, which indicates the information content of the data and provides an objective basis for comparison of different data sets. Further, only with quantitative uncertainty estimation can issues such as resolving parameter spatial variability or frequency dependence be addressed in a meaningful manner (e.g., differentiating uncertainty from variability). Parameter uncertainty estimation in nonlinear inversion is itself a challenging research problem. This proposal is based on a Bayesian inference formulation and a numerical (statistical) sampling approach, under development, that builds up probability distributions for the unknown seabed parameters based on the data and available physical constraints (prior information).