Inversion of acoustic data using a combination of genetic algorithms and the Gauss–Newton approach

Abstract

An inversion procedure for obtaining speeds, attenuation, densities, and thicknesses for a layered medium is described. The inversion is carried out using the least-squares technique and the forward modeling is based on SAFARI. The optimization is a hybrid method combining the global genetic algorithms and the local Gauss–Newton method. This is done by taking several gradient steps between each update of the object function for each ‘‘individual’’ in the population. The gradients for the Gauss–Newton method are computed analytically; this makes the computation faster and more stable than computing the gradients by numerical differentiation. The combination of a global and a local method makes the hybrid method faster and it gets closer to the global minimum than a pure global method. Examples based on both real and synthetic data in wave-number-frequency and range-frequency domains show that the method works well.