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In the present study, we test a nature-inspired, meta-heuristic, global search optimization algorithm to infer the 1D/2D shear wave velocity (Vs) profile from the synthetic data (using SOFI2D) and field data (across the Himalayan Frontal Thrust (HFT)) containing surface (Rayleigh) waves. We first implement the mentioned methodology on the synthetic short gather (for this velocity-depth model is known) to gain confidence on the inverted Vs profiles. For the synthetic data, we generate the dispersion image (using Phase-shift method) and extract the experimental fundamental mode dispersion curve. We then successfully implemented the Particle swarm optimization algorithm (PSO) with a linearly decreasing inertia weight variant to invert the obtained dispersion data and finally deduce the global optimal Vs profiles. Using PSO as a global search optimization algorithm we report high confidence, as the inversion result shows a good match between the true and inverted 1D Vs profiles for the synthetic data. Finally, we implement it to the field datasets acquired across the HFT at Pawalgarh in Uttarakhand, India to infer the shear wave velocity and shear modulus variation.