Inversion of Rayleigh Wave Dispersion Curves (RWDCs) via Improved Salp Swarm Algorithm (ISSA)

Abstract

In engineering survey practice, the inversion analysis of Rayleigh Wave Dispersion Curves (RWDCs) has become a key technical means for obtaining the physical and mechanical parameters of subsurface. However, traditional global optimization algorithms generally face bottlenecks such as low convergence efficiency, limited solution accuracy, and susceptibility to premature convergence in this inversion process. To address this, this paper proposes an improved RWDCs inversion model based on the Improved Salp Swarm Algorithm (ISSA): the Tent chaotic mapping is used to initialize the population configuration, enabling precise control over the initial spatial distribution of the population; combined with an adaptive update mechanism, this dynamically balances the algorithm’s global exploration and local optimization performance. To validate the applicability and reliability of the ISSA in obtaining subsurface shear wave velocity structures through RWDCs inversion, a practical experimental design was developed: systematic inversion tests were conducted using three simulated scenarios based on theoretical geological models and two actual engineering survey data scenarios. The experimental results show that ISSA can be stably applied to the RWDCs inversion process and demonstrates excellent inversion performance. Compared with the classical Particle Swarm Optimization (PSO), ISSA exhibits significant advantages in terms of inversion efficiency (convergence speed), solution accuracy (parameter agreement), and numerical stability. Joint inversion results using active source data from Iceland and active-passive source data from Italy demonstrate the high reliability of ISSA inversions, indicating that this method can provide efficient and precise algorithmic support for the quantitative interpretation of RWDCs.