Stochastic characterization of acoustic signals for sequential dispersion tracking and geoacoustic inversion

Abstract

In previous work, we had shown how sequential Bayesian filtering methods can be used for the successful extraction of dispersion curves from broadband long-range acoustic data. Here, we extend this work by tracing carefully the true nature of the noise and the resulting probability density observations of the spectrogram of the received time series, employed in the tracking. The Gaussian model typically used in instantaneous frequency tracking relies on the assumption that noise is additive in the frequency domain. This model is, however, inaccurate. We discuss a chi-squared model of the acoustic data perturbations and its role in dispersion curve tracking. The new method provides much clearer curves than those computed with previous approaches. We demonstrate the potential of the technique by applying it to synthetic and real data for dispersion curve estimation and bathymetry and sediment sound speed inversion. [Work supported by ONR.]