Genetic algorithm for conductivity imaging of airborne electromagnetic data

We present a genetic algorithm (GA) for solving an ill-posed inverse problem from exploration geophysics, namely the estimation of a distribution of conductivities from a set of electrical current penetration depths. We formulate the inversion as a Bayesian inference problem and use a GA to efficiently sample the posterior parameter distribution. In particular, the conductivity distribution with maximum entropy relative to the observed data is estimated. The method is illustrated on an airborne electromagnetic data set collected over the Karoo, South Africa.