Enhancing resolution of seismic data based on waveform inversion with cooperative coevolutionary differential evolution algorithm

In this paper, a new global optimization algorithm, cooperative coevolutionary differential evolution (CCDE), was proposed for seismic waveform inversion and applied to enhancing the resolution of seismic data. To invert a high resolution impedance section or seismic section, the earth model must be finely parameterized with large number of parameters. But the conventional global optimization algorithms often lose their effectiveness and advantages when applied to high-dimensional and complex problems. CCDE is effective and robust for high-dimensional waveform inversion. It decomposes the high-dimensional model into some subcomponents. Then another novel feature of CCDE is that, it introduces a local fitness function for each subcomponent, and a new mutation operator is designed to guide the mutation direction of each subcomponent with the corresponding local fitness value. Compare with the commonly used resolution-enhancement methods, our method can remove internal multiples and needs no assumptions for the wavelet and earth model.