利用纵波同时估计海床及其粗糙度
Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves
AI总结：
本文提出基于波散射的无限维贝叶斯框架，用于声学海床断层成像，通过统计各向同性研究海床粗糙度，提供稳健算法以估计海床并量化不确定性。
摘要：
本文介绍了一个无限维的贝叶斯框架，用于声学海床断层成像，利用波散射同时估计海床及其粗糙度。断层成像被视为一个病态问题，其中多种海床配置可能导致相似的测量模式。我们提出了一种新的方法，专注于海床的统计各向同性。利用分数可导性来识别海床粗糙度，本文提出了一种稳健的数值算法来估计海床并量化不确定性。广泛的数值实验验证了该方法的有效性，为大规模海床探索提供了有前途的途径。
英文摘要：
This paper introduces an infinite-dimensional Bayesian framework for acoustic seabed tomography, leveraging wave scattering to simultaneously estimate the seabed and its roughness. Tomography is considered an ill-posed problem where multiple seabed configurations can result in similar measurement patterns. We propose a novel approach focusing on the statistical isotropy of the seabed. Utilizing fractional differentiability to identify seabed roughness, the paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties. Extensive numerical experiments validate the effectiveness of this method, offering a promising avenue for large-scale seabed exploration.
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