Geoacoustic inversion via genetic algorithm and its application to manganese sediment identification

An acoustic inversion method using a wine-band signal and two near field receivers is proposed and applied to multiple layered seabed models including a manganese sediment. The inversion problem can be formulated into a probabilistic model comprised of signals, a forward model, and additive noise. The forward model simulates wide-band signals, such as chirp signals, and is chosen to be the source-wavelet-convolution plane wave modeling method. The wavelet matching technique, using weighted least-squares-fitting, estimates the sediment sound-speed and thickness on which determination of the possible numerical ranges for a priori uniform distribution is based. The genetic algorithm is applied to a global optimization problem to find a maximum a posteriori solution for determined a priori search space. Here the object function is defined by an L-2 norm of the difference between measured and modeled signals. Not only the marginal pdf but also ifs statistics are calculated by numerical evaluation of integrals using the samples selected during importance sampling process of the genetic algorithm.