Application of improved sine cosine algorithm with chaotic mapping and novel updating methods for joint inversion of resistivity and surface wave data

To address the challenges associated with the joint inversion of Rayleigh wave dispersion curves and Vertical Electrical Sounding (VES) data, we propose an Improved Sine Cosine Algorithm (ISCA). The original Sine Cosine Algorithm (SCA), which uses sine and cosine functions for optimization, suffers from limitations such as suboptimal control parameter selection and inadequate population diversity during evolution. To overcome these issues, we introduce several enhancements: chaotic mapping for population initialization, an exponential position updating strategy, and a novel offspring generation mechanism. We first assess ISCA’s optimization capabilities by testing it on five complex benchmark functions, where it consistently outperforms the original SCA. We then apply SCA, ISCA, and the classical Particle Swarm Optimization (PSO) algorithm to the joint inversion of Rayleigh wave dispersion and VES data under controlled geological models, both with and without noise. The results demonstrate that ISCA offers improved accuracy and robustness in handling joint inversion problems. When applied to field data from the Besirli site in Türkiye, ISCA outperforms both SCA and PSO, yielding lower fitting errors, better alignment with prior geophysical interpretations, and enhanced solution stability. These results underscore the effectiveness of ISCA for the joint inversion of Rayleigh wave and VES data and highlight its broader potential in geophysical inversion tasks. Furthermore, the integration of these two datasets provides complementary constraints that significantly improve the reliability and resolution of subsurface models.