Most of the continental shelf area is a weakly range-dependent shallow-water environment. Compared with range-independent Bayesian geoacoustic inversion, range-dependent inversion usually has problems with the complex forward model and low efficiency for posterior analysis. According to the adiabatic normal-mode theory, the weakly range-dependent shallow-water environment can be divided into a series of range-independent segments; thus, this paper proposes a dual-source modal dispersion inversion method for local geoacoustic parameters of a segment based on a range-independent forward model. In addition, considering that the computational cost of the forward model limits the application of sampling-based methods for posterior analysis, a novel approximate variational inference, namely variational Bayesian Monte Carlo, is applied in this study. It has superior efficiency and shows similar accuracy compared with Markov Chain Monte Carlo sampling. This work is demonstrated in the shallow-water experiment in the continental shelf area of the East China Sea, and the results indicate that the local and range-dependent geoacoustic parameters are well-estimated.

大部分大陆架区域是弱范围依赖的浅水环境。与距离无关的贝叶斯地声反演相比，距离相关的反演通常存在正演模型复杂、后验分析效率低的问题。根据绝热简正模态理论，可以将弱距离相关的浅水环境划分为一系列距离无关的段；为此，本文提出一种基于距离无关正演模型的分段局部地声参数双源模态频散反演方法。此外，考虑到前向模型的计算成本限制了基于采样的后验分析方法的应用，本研究采用了一种新颖的近似变分推理，即变分贝叶斯蒙特卡罗。与马尔可夫链蒙特卡罗采样相比，它具有卓越的效率并显示出相似的精度。这项工作在东海大陆架区域的浅水实验中得到了验证，结果表明局部和范围相关的地声参数得到了很好的估计。