Compressive acoustic sound speed profile estimation using wavelets
Compressive acoustic sound speed profile estimation using wavelets
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DOI：
10.1121/1.4950426
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发表时间：
2016-04
影响因子：
2.4
通讯作者：
Michael J. Bianco;Haiqiang Niu;P. Gerstoft
中科院分区：
文献类型：
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作者：
Michael J. Bianco;Haiqiang Niu;P. Gerstoft
估算海洋声速分布需要对有限观测值的声学数据进行反演。真实海洋SSP结构的反演是一个非线性的、欠定的问题，需要正则化以确保物理上真实的解。传统的正则化，使最佳拟合解的能量最小化，需要对真正的ssp进行欠采样或使用很少的形状函数。这将导致低分辨率的SSP估计，从而影响地球声反演中其他参数的精度。压缩感知（CS）可靠地估计某些高度欠确定线性问题的信号参数，前提是信号可以“压缩”：在稀疏域中表示，其中很少有非零系数（从许多）解释观测值。本文表明，海洋ssp可以使用小波字典、经验正交函数和给定先验环境统计的其他形状函数来压缩。寻找代表ssp的形状字典……
Estimation of ocean acoustic sound speed profiles (SSPs) requires inversion of acoustic data with limited observations. Inversion for true ocean SSP structure is a nonlinear, underdetermined problem that requires regularization to ensure physically realistic solutions. Traditional regularization, which minimizes the energy of best-fit solutions, requires undersampling of true SSPs or using few shape functions. This gives low resolution SSP estimates which can affect the accuracy of other parameters in geoacoustic inversion. Compressive sensing (CS) reliably estimates signal parameters for certain highly underdetermined linear problems provided the signal can be “compressed:” represented in a sparse domain where few non-zero coefficients (out of many) explain the observations. Here, it is shown that ocean SSPs can be compressed using dictionaries of wavelets, empirical orthogonal functions, and other shape functions given a priori environmental statistics. Shape dictionaries are sought which represent SSPs...