Seismic velocity inversion with genetic algorithms

We use genetic algorithms to find geologically plausible sub-surface models from seismic travel-time data. Given a sub-surface model, the physics of wave propagation through refractive media can be used to compute travel times for seismic waves. However, in practice, we have to solve the inverse problem: travel-times are available and the problem is to infer sub-surface structure. This inverse problem is fundamental to seismology. To determine the suitability and applicability of genetic algorithms to this seismic inversion problem, we tested a number of different genetic algorithm parameters and operators. Experiments with two synthetic seismic models shows that large population sizes are critical to generating good seismic velocity models and that our two-dimensional crossover operators always performed better than one-dimensional crossover. The genetic algorithms also produces models that fit the data better than models produced by simulated annealing. We believe that our results together with the easy parallelizability of genetic algorithms make a strong case for their use in seismic inversion