The inversion of Rayleigh wave dispersion curves based on the snake optimization algorithm

Dispersion curve inversion is a core component of Rayleigh wave surveys, primarily involving local linear optimization algorithms and global nonlinear optimization algorithms. Local linear optimization algorithms exhibit poor adaptability when addressing complex nonlinear features, making it challenging to identify the global optimum. The introduction of global nonlinear optimization algorithms enhances the accuracy of finding the global optimum and helps avoid the issue of converging to local minima. However, traditional global nonlinear optimization algorithms, such as genetic algorithms and particle swarm optimization (PSO), often demonstrate instability and slow convergence rates in the presence of noisy data. To address these issues, the snake optimization algorithm (SOA) is introduced first, which simulates the hunting paths of snakes to effectively search the parameter space and optimize the fitting of dispersion curves. Then, three theoretical models are constructed for inversion to verify the stability and noise resistance of the algorithm. Finally, SOA is applied to field data from a region in Jiangxi, China. The results indicate that, compared to PSO and whale optimization algorithm, the inversion results from SOA are closer to the actual borehole data, further demonstrating the algorithm's robustness against noise and its practicality in addressing engineering and geological applications.