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

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