Pre-stack seismic waveform inversion based on adaptive genetic algorithm

Pre-stack waveform inversion, by inverting seismic information, can estimate subsurface elastic properties for reservoir characterization, thus effectively guiding exploration. In recent years, nonlinear inversion methods, such as standard genetic algorithm, have been extensively adopted in seismic inversion due to its simplicity, versatility, and robustness. However, standard genetic algorithms have some shortcomings, such as slow convergence rate and easiness to fall into local optimum. In order to overcome these problems, the authors present a new adaptive genetic algorithm for seismic inversion, in which the selection adopts regional equilibrium and elite retention strategies are adopted, and adaptive operators are used in the crossover and mutation to implement local search. After applying this method to pre-stack seismic data, it is found that higher quality inversion results can be achieved within reasonable running time.