Application of genetic algorithms to an inversion of surface-wave dispersion data

TLDR

Surface‑wave inversion algorithms typically involve selection, crossover, and mutation of individuals in a population. The study introduces a new method for inverting surface‑wave dispersion data. The method employs genetic algorithms with elite selection and dynamic mutation to globally optimize surface‑wave dispersion inversions, and is tested on synthetic and observed data to recover S‑wave profiles of sedimentary layers. The method proved robust and effective for interpreting surface‑wave dispersion data.

Abstract

Abstract A new method for inversion of surface-wave dispersion data is introduced. This method successfully utilizes recently developed genetic algorithms as a global optimization method. Such algorithms usually consist of selection, crossover, and mutation of individuals in a population. To facilitate convergence to an optimal solution, we added elite selection, which ensures that the “best” individual with the smallest misfit value is not excluded from the succeeding generation, and dynamic mutation, which contains a generation-variant mutation probability. Using synthetic and observed earthquake data, we examined the applicability of this genetic surface-wave inversion method in deducing an S-wave profile for sedimentary layers from short- and intermediate-period surface-wave dispersion data. We demonstrated that the method is robust and can be used to interpret surface-wave dispersion data.