An adapted eigenvalue-based filter for ocean ambient noise processing

Abstract

Accurate approximations of Green’s functions retrieved from the correlations of ambient noise require a homogeneous distribution of random and uncorrelated noise sources. In the real world, the existence of highly coherent, strong directional noise generated by ships, earthquakes, and other human activities can result in biases in the ambient-noise crosscorrelations (NCCs). We have developed an adapted eigenvalue-based filter to attenuate the interference of strong directional sources. The filter is based on the statistical model of the sample covariance matrix and can separate different components of the data covariance matrix in the eigenvalue spectrum. To improve the effectiveness and make it adaptable for different data sets, a weight is introduced to the filter. Then, the NCCs can be calculated directly from the filtered data covariance matrix. This approach is applied to a 1.02 h data set of ambient noise recorded by a permanent reservoir monitoring receiver array installed on the seabed. The power spectral density indicates that the noise recordings were contaminated by strong directional noise over nearly half of the whole observation period. Beamforming and crosscorrelation results indicate that the interference still exists even after applying traditional temporal and spectral normalization techniques, whereas the adapted eigenvalue-based filter can significantly attenuate it and help to obtain improved crosscorrelations. The approach makes it possible to retrieve reliable approximations of Green’s functions over a much shorter recording time.

Shear-Wave Tomography Using Ocean Ambient Noise with Interference

“…Events similar to A, B, and C are unavoidable and unpredictable in the real world, challenging the accuracy and stability of the PSI. The adapted eigenvalue-based filter can help to mitigate the influence of these events and obtain improved NCC [22]. We apply the adapted eigenvalue-based filter to the noise data recorded by the PRMRA.…”

“…We apply the adapted eigenvalue-based filter to the noise data recorded by the PRMRA. In the following subsections, we summarize the basic ideas of the adapted eigenvalue-based filter (a detailed description can be found in [22]) and retrieve the NCC from the contaminated noise data using the adapted eigenvalue-based filter.…”

“…One of the key tasks of PSI is the reliable approximation of Green's functions between receiver pairs. It requires that the noise field should be diffuse and equipartitioned [21,22]. Unfortunately, the noise field is usually contaminated by strong, directional sources in the real world, introducing biases into the approximated Green's functions [23][24][25][26][27].…”

“…Unfortunately, the noise field is usually contaminated by strong, directional sources in the real world, introducing biases into the approximated Green's functions [23][24][25][26][27]. Wu et al [22] reviewed the techniques which have been proposed to attenuate the interference of strong directional sources, and developed an adapted eigenvalue-based filter to improve the quality of the estimation of the Green's function. The filter is adaptable for different data sets and can reduce the influence of strong, directional sources significantly.…”

“…The adapted eigenvalue-based filter is applied as a pre-processing method to suppress the strong, directional interference in the recording and retrieve reliable approximations of Green's functions. Note that the paper focuses on the same dataset with Wu et al [22] but Wu et al [22] focuses on the analysis of noise recording at one sensor, while this paper focuses on the whole data array.…”