Optimized extraction of coherent arrivals from ambient noise correlations in a rapidly fluctuating medium, with an application to passive acoustic tomography

Abstract

Ambient noise correlations can be used to estimate Green’s functions for passive monitoring purposes. However, this method traditionally relies on sufficient time-averaging of the noise-correlations to extract coherent arrivals (i.e., Green’s function estimates), and is thus limited by rapid environmental fluctuations occurring on short time scales while the averaging takes place. For instance, based on extrapolating results from a previous study [Woolfe et al., 2015], passive ocean monitoring across basin scales (i.e., between hydrophones separated by ∼1000 km) may require at least 10 weeks of averaging time to extract coherent arrivals; but such an averaging time would be too long to capture some aspects of the mesoscale variability of the ocean. To address this limitation, we will demonstrate with simulation and data that the use of a stochastic search algorithm to correct and track these rapid environmental fluctuations can reduce the required averaging time to extract coherent arrivals from noise correlations in a fluctuating medium. The algorithm optimizes the output of an objective function based on a matched filter that uses a known reference waveform to track a set of weak coherent arrivals buried in noise.