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Matched field processing is a powerful tool for source localization and geoacoustic inversion. Because of significant environmental and geometry uncertainties, however, matched field processing usually involves multiparameter searches. To facilitate these searches, global optimization techniques such as genetic algorithms and simulated annealing have been successfully employed. In this work, a different approach, tabu, is implemented for optimization in matched field inversion. Tabu is a technique relying on the use of memory; it searches for the global maximum of the objective function through a navigation process that avoids already revisited models, also making use of aspiration criteria and diversification for faster convergence. The tabu performance in localization and geoacoustic inversion is demonstrated through experimentation with both synthetic and real (SWellEX 96) data. The approach is shown to provide reliable estimates in an efficient manner. [Work supported by ONR.] |
