Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions

The goal of many underwater acoustic modeling problems is to find the physical parameters of the environment. With the increase in computer power and the development of advanced numerical models it is now feasible to carry out multiparameter inversion. The inversion is posed as an optimization problem, which is solved by a directed Monte Carlo search using genetic algorithms. The genetic algorithm presented in this paper is formulated by steady-state reproduction without duplicates. For the selection of ‘‘parents’’ the object function is scaled according to a Boltzmann distribution with a ‘‘temperature’’ equal to the fitness of one of the members in the population. The inversion would be incomplete if not followed by an analysis of the uncertainties of the result. When using genetic algorithms the response from many environmental parameter sets has to be computed in order to estimate the solution. The many samples of the models are used to estimate the a posteriori probabilities of the model parameters. Thus the uniqueness and uncertainty of the model parameters are assessed. Inversion methods are generally formulated independently of forward modeling routines. Here they are applied to the inversion of geoacoustic parameters (P and S velocities and layer thickness) in the bottom using a horizontally stratified environment. The examples show that for synthetic data it is feasible to carry out an inversion for bottom parameters using genetic algorithms.