Range-dependent matched-field inversion of SWellEX-96 data using the downhill simplex algorithm

Abstract

Matched-field inversion (MFI) techniques have been applied for effective and efficient estimation of geoacoustic parameters of the ocean bottom. This paper presents a new tomographic MFI method for use in range-dependent environments. The MFI correlates modeled data with measured data and uses a search algorithm to determine model parameter values that maximize the correlator. In the present method, a parabolic equation propagation model is used to compute replica fields to account for mode coupling in the environment. The search algorithm is a two-stage hybrid method that combines an initial global component and a final local component. The first stage employs a random search to determine N+1 parameter sets with the best correlations, where N is the number of parameters being determined. In the next stage the N+1 sets are used as inputs to the local downhill simplex algorithm. The algorithm is shown to perform well for simulated vertical line array data for an environment representative of the SWellEX-96 experimental site. The inversion technique is then applied to measured data obtained from a radial track in SWellEX-96. The geometric parameters of the experiment and dominant geoacoustic parameters were successfully recovered for data with relatively low signal-to-noise ratio.