Inversion of Rayleigh wave dispersion curve via adaptive logarithmic Spiral-Levy firefly algorithm

Rayleigh wave technique is one of the important means of shallow surface geological survey. The underground shear wave velocity model can be effectively obtained through the inversion of Rayleigh wave dispersion curve. However, Rayleigh wave dispersion curve inversion has the characteristics of multi-parameter, multi-extreme and nonlinear. Facing the Rayleigh wave data processing under complex seismic-geological conditions, the traditional methods are difficult to invert and reconstruct formation parameters quickly and accurately. In this paper, the Adaptive Logarithmic Spiral-Levy Firefly Algorithm (ALSL-FA) was introduced into the inversion of Rayleigh wave dispersion curve. ALSL-FA effectively solves the shortcomings of the classical Firefly Algorithm (FA) that is good at exploration and poor at development. ALSL-FA integrates log spiral to guide the path of firefly, and realizes the adaptive switching between global search and local development through adaptive switching factor in the search process, which enhances the ability of global search while enhancing the ability of local development. The computational performance of FA, Levy Flying Firefly Algorithm (LF-FA) and ALSL-FA was compared and analyzed by solving the Rastrigin function. The inversion results of Artificial Bee Colony algorithm (ABC), FA, LF-FA and ALSL-FA were compared through the inversion of Rayleigh wave dispersion curve of velocity increasing and high-velocity interlayer models while the correctness and anti-noise ability of our method were verified. And then, it was further applied to a real Rayleigh wave dataset inversion processing. Our research results show that the method effectively weakens the dependence on the initial model, and further improves the inversion accuracy. It also has strong global optimization ability and local development inversion accuracy. It has strong global optimization ability and local development ability as while as good anti-noise ability.