A hybrid simplex genetic algorithm for estimating geoacoustic parameters using matched-field inversion

Matched-field inversion (MFI) undertakes to estimate the geometric and geoa coustic parameters in an ocean acoustic scenario by matching acoustic held data recorded at a hydrophone array with numerical calculations of the held . The model which provides the best fit to the data is the estimate of the actual experimental scenario. MFI provides a comparatively inexpensive meth od for estimating ocean bottom parameters over an extensive area, The basic components of the inversion process are a sound propagation model and matc hing (minimization) algorithm. Since a typical MFI problem requires a large number of computationally intensive sound propagation calculations, both o f these components have to be efficient. In this study, a hybrid inversion algorithm which uses a parabolic equation propagation model and combines th e downhill simplex algorithm with genetic algorithms is introduced. The alg orithm is demonstrated on synthetic range-dependent shallow-water data gene rated using the parabolic equation propagation model. The performance for e stimating the model parameters is compared For realistic signal-to-noise ra tios in the synthetic data.