Enhancing ambient noise correlation processing using vector sensors.

Abstract

Ambient noise cross-correlations between separated sensors can yield estimates of the Green's function between them. Vector sensors (which record both pressure and acoustic velocity vector components) can leverage their directionality to reject ambient noise sources that do not contribute to the emergence of the Green's function, thus improving performance over standard omnidirectional hydrophones. To quantify this performance gain, a time-domain analytical expression for the correlation between each component of a vector sensor in the presence of an isotropic ambient noise field is derived. Improvement of the velocity channel correlations relative to pressure channel correlations is examined for varying bandwidth, sensor separation distance, and additive channel noise levels. Last, the experimentally measured reduction in variance for the velocity channels correlations vs pressure correlations, using drifting vector sensors deployed in the Long Island Sound, were found to be comparable to the theoretical prediction. Overall, both theoretical and experimental results indicate modest gains are obtained when extracting the Green's function from velocity correlations over using pressure correlations. Thus, vector sensors can be used to reduce the required averaging time for this noise correlation processing, which may be especially useful, for instance, in a fluctuating environment or for drifting sensors.