Abstract
The empirical Green’s function obtained from cross‐correlations of ambient noise has several applications such as passive imaging, acoustic monitoring, remote sensing, etc. In general, long periods of averaging are necessary to obtain stable estimates. Short time averages suffer from directional biases due to the changing noise field and the dominating effect of loud sources such as ships, in addition to having a low signal to noise ratio. In this paper we analyze the eigenvalues and eigenvectors of the cross‐spectral density matrix from short time estimates. Recent results in statistical theory are used to separate the random noise component from meaningful information. By retaining only those eigenvalues and eigenvectors that contribute toward the buildup of Green’s function, we can reduce the time required to obtain a stable estimate and achieve higher signal to noise ratios. Results are presented from the SW06 data.
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