Improvements in the genetic algorithm inversion of receiver functions using extinction and a new selection approach

Despite the robustness of standard genetic algorithms in receiver functions inversion for crustal and uppermost mantle velocity-depth structure, one drawback is that towards the end of a 'run', only a few variations in solution ideas are explored. This may lead to the stagnation of the optimization process and can be a major drawback for large model dimensions. To mitigate this problem, we introduced a new selection method that retains the best features of explored models, with an extinction procedure that increases the exploration of the model space through the principle of self-organized criticality. We test the performance of the modified genetic algorithm technique by applying it to the inversion of synthetically generated receiver functions for crustal velocity structure and comparing the results with those obtained using a standard genetic algorithm. The test cases involve using 2 different objective functions, based on the L2 norm and cosine similarity, with 2 different model parameterizations of different model sizes. The results show that our modified genetic algorithm improves the inversion process by consistently obtaining best models with the lowest misfit values and a distribution of best models with less deviations from the true model values. With an improvement of computation time of up to 11.2%, the results suggest that the modified genetic algorithm is best suited to obtain higher accuracy results in shorter computation times which will be especially useful for higher dimension models needing larger pool sizes.