稀疏贝叶斯学习用于噪声互相关提取格林函数

在较窄频带条件下,传统的噪声互相关提取时域格林函数(Time Domain Green’ s Function, TDGF)方法分辨率低，影响了海洋被动声层析的应用。针对这一问题，提出了一种基于稀疏贝叶斯学习(Sparse Bayesian Learning,SBL)的噪声互相关提取 TDGF的方法。首先，构造了 TDGF的稀疏表示模型，其中字典矩阵由傅里叶变换算子构成，观测矩阵由频域噪声互相关函数组成。然后，使用预累积处理来折中 SBL估计 TDGF的分辨率与稳定性。仿真与海试实验数据表明，联合预累积处理的 SBL方法有效地从较窄频带的海洋环境噪声中提取了传统方法无法分辨的TDGF到达时间，从而为自适应选取噪声频段、实现快速海洋被动声层析提供了一种可行思路。

Sparse Bayesian learning for Green's function extraction from noise cross-correlations

The traditional method of time domain Green''s function (TDGF) extraction from noise cross-correlations has low resolution in a relative narrow frequency band, which affects the application of passive ocean acoustic tomography. In order to solve the problem, a method to estimate TDGF from ambient noise cross-correlations by sparse Bayesian learning (SBL) is proposed in this paper. Firstly, the sparse representation model for TDGF extraction is established, in which, the dictionary matrix consists of Fourier transform operators and the observation matrix consists of the noise cross-correlation functions in the frequency domain. Then a pre-stacking technique is adopted to compromise the resolution and the robustness of SBL for TDGF extraction. Simulation and experimental data in shallow water show that the pre-stacking-based SBL can effectively extract the arrival structure of TDGF from ambient noise with a relatively narrow frequency band, which cannot be distinguished by the traditional method, thereby a way is provided for adaptively selecting noise frequency bands to realize fast passive ocean acoustic tomography is provided.