Bayesian geoacoustic parameters inversion for multi-layer seabed in shallow sea using underwater acoustic field

海床 地质学 水下 声学 模拟退火 水声学 反演(地质) 贝叶斯概率 计算机科学 算法 地震学 海洋学 人工智能 物理 构造学
作者
Yangyang Xue,Han-hao Zhu,Xiaohan Wang,Guangxue Zheng,Xu Liu,Jiahui Wang
出处
期刊:Frontiers in Marine Science [Frontiers Media SA]
卷期号:10
标识
DOI:10.3389/fmars.2023.1058542
摘要

Seabed geoacoustic parameters play an important role in underwater acoustic channel modeling. Traditional methods to determine these parameters, for example, drilling, are expensive and are being replaced by acoustic inverse technology. An inversion method based on Bayesian theory is presented to derive the structure and geoacoustic parameters of a layered seabed in a shallow sea. The seabed was considered a layered elastic medium. The objective of this research was to use the sound pressure detected by underwater acoustic sensors at different positions and to use nonlinear Bayesian inversion to estimate the geoacoustic parameters and their uncertainties in the multi-layer seabed. Specifically, the thickness, density, compression wave speed, shear wave speed, and the attenuation of these two wave speeds were determined. The maximum a posterior (MAP) model and posterior probability distribution of each parameter were estimated using the optimized simulated annealing (OSA) and Metropolis-Hastings sampling (MHS) methods. Model selection was carried out using the Bayesian information criterion (BIC) to determine the optimal model that thoroughly explained the experimental data for different parameterizations. The results showed that the OSA is much more capable of delivering high-accuracy results in multi-layer seabed models. The compression wave speed and shear wave speed were less uncertain than the other parameters, and the parameters in the upper layer had less uncertainty than those in the lower layer.

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