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

Abstract

Matched-field inversion ͑MFI͒ 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 Nϩ1 parameter sets with the best correlations, where N is the number of parameters being determined. In the next stage the Nϩ1 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.

Matched-impulse-response processing for shallow-water localization and geoacoustic inversion

“…Searches can be exhaustive or can be combined with global optimization techniques for a more efficient ͑but, occasionally, with a suboptimal, local maximum result͒ exploration of the parameter space. [8][9][10][11][12][13][14][15] In this paper, for the inversion for the source location and geoacoustic parameters we use a modified grid search, an efficient hierarchical search scheme that first inverts for the most important parameters of the acoustic field model and then refines the inversion process by searching over the secondary parameters.…”

Tabu for matched-field source localization and geoacoustic inversion

“…[10][11][12][13][14][15][16]. Approaches such as neural networks 17,18 and the simplex algorithm 19 have also been tested on MFP. Optimization of posterior probability distribution calculations with Monte Carlo integration 20 or Gibbs sampling 21 have been proposed as well.…”

Range-dependent geoacoustic inversion of vertical line array data using matched beam processing

“…4,5 On the contrary, a VLA configuration is relatively stable and capable of receiving sound fields from all directions and can probe large areas around the VLA with a towed source or multiple sources. [6][7][8][9][10][11] Although there is some computational complexity in propagation modeling, VLA configurations are still effective in range-dependent inversion.…”

“…The MF inversion has been successfully applied to estimate bottom characteristics in various shallow water environments. [1][2][3][4][5][6][7][8][9][10][11][12] Actual shallow water environments usually have range variability in seabed properties as well as in bathymetry or ocean sound speed. However, in conventional geoacoustic inversions using a vertical line array ͑VLA͒ and a single source, the geoacoustic properties have been typically assumed to be range independent.…”

“…To prevent such degradation in inversion modeling accuracy, research has been conducted on rangedependent geoacoustic inversion. [4][5][6][7][8][9][10][11][12] Towed horizontal line array ͑HLA͒ schemes are one such range-dependent inversion method. The towed HLA system has several advantages over a VLA, such as mobility and manageability, and provides simplicity of the inversion modeling based on assuming range independency in local regions between the source and receiver.…”