Weighting‐Free Multi‐Objective Global Optimization for Transdimensional Joint Inversion of Surface Wave Dispersion, Refraction, and Resistivity Data in Near‐Surface Characterization

We present a practical joint inversion framework for active‐source Rayleigh‐wave dispersion curves, refraction traveltimes, and electrical resistivity data to improve the accuracy and reliability of near‐surface characterization. The framework uses a multi‐objective global optimization strategy that avoids prescribing subjective weights in a single combined objective function. We develop a multi‐objective Modified Barnacles Mating Optimizer by integrating Pareto dominance into the optimizer which enables simultaneous minimization of multiple data‐misfit measures. Prior to inversion, modal and parameter‐sensitivity analyses are conducted on a synthetic model. The experiments show that the problem is highly nonlinear, uncertainty‐prone and characterized by strongly heterogeneous parameter sensitivities. The proposed approach is validated using synthetic data and benchmarked against conventional single‐objective inversions using equal‐weighting and randomly assigned weights under identical settings, and it is further demonstrated on some real datasets from Türkiye. To support transdimensional inference, we also use a multiple‐model space strategy that allows switching among model parameterizations without substantially increasing search complexity. Field results are interpreted using available geological and geophysical information, and post‐inversion uncertainty analyses are conducted to assess solution credibility. The proposed framework provides a broadly applicable methodology for site characterization in geologically complex environments using transdimensional joint inversion.