Ship source level estimation in an uncertain environment via trans-dimensional Bayesian inversion

Abstract

This paper considers the joint estimation of ship source spectral levels and environment parameters of a layered seabed via a trans-dimensional Bayesian matched-field inversion approach, with applications to shallow-water data collected with acoustic arrays in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The approach samples probabilistically over possible model parameterizations (number of seabed layers), and provides uncertainty estimates of ship source levels that include uncertainty due to the environment and source depth/range. Approaches to modeling of a distributed source (i.e., as multiple point sources) in the inversions will also be considered. The approach is applied to low-frequency tonal (propeller and machinery) noise in the 10–300 Hz frequency band due to large container ships passing near the arrays.This paper considers the joint estimation of ship source spectral levels and environment parameters of a layered seabed via a trans-dimensional Bayesian matched-field inversion approach, with applications to shallow-water data collected with acoustic arrays in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The approach samples probabilistically over possible model parameterizations (number of seabed layers), and provides uncertainty estimates of ship source levels that include uncertainty due to the environment and source depth/range. Approaches to modeling of a distributed source (i.e., as multiple point sources) in the inversions will also be considered. The approach is applied to low-frequency tonal (propeller and machinery) noise in the 10–300 Hz frequency band due to large container ships passing near the arrays.