Improved sparrow search Algorithm (ISSA)-based joint inversion of refraction and Rayleigh wave dispersion: a case of seawater intrusion

Abstract Seismic methods play a key role in assessing subsurface conditions in near-surface geophysical exploration. To address the challenges of jointly inverting Rayleigh wave dispersion curves and seismic refraction data, this work introduces an improved sparrow search algorithm (ISSA) with two key enhancements. Tent chaotic mapping combined with opposition-based learning was used for population initialization. This significantly enhanced diversity compared with the standard random initialization in the original sparrow search algorithm (SSA). To enhance the trade-off between global search and local refinement, the algorithm integrates the Lévy flight mechanism. Algorithm performance was evaluated using a two-stage validation process. Benchmark tests on four complex multimodal functions showed that the ISSA achieves faster and more stable convergence than the original SSA. Synthetic inversion experiments conducted under both noise-free and noisy conditions confirm the superior robustness of the ISSA for joint inversion, outperforming both SSA and particle swarm optimization. An application to field data from a coastal aquifer in Türkiye affected by saltwater intrusion further validates the proposed approach, showing reduced misfit errors, improved agreement with independent geophysical interpretations, and greater solution stability. By integrating dispersion and refraction data, the ISSA benefits from complementary constraints that help mitigate the ambiguities commonly encountered in single-method inversions. These findings highlight the effectiveness of the ISSA for near-surface geophysical applications and its adaptability to broader multi-parameter inversion problems.