Acquiring surface seismic data can be challenging in areas of intense human activities, due to presence of infrastructures (roads, houses, rigs), often leaving large gaps in the fold of coverage that can span over several kilometers. Modern interpolation algorithms can interpolate up to a certain extent, but quality of reconstructed seismic data diminishes as the acquisition gap increases. This is where vintage seismic acquisition can aid processing and imaging, especially if previous acquisition did not face the same surface obstacles. In this paper we will present how the legacy seismic survey has helped to fill in the data gaps of the new acquisition and produced improved seismic image. The new acquisition survey is part of the Mega 3D onshore effort undertaken by ADNOC, characterized by dense shot and receiver spacing with focus on full azimuth and broadband. Due to surface infrastructures, data could not be completely acquired leaving sizable gap in the target area. However, a legacy seismic acquisition undertaken in 2014 had access to such gap zones, as infrastructures were not present at the time. Legacy seismic data has been previously processed and imaged, however simple post-imaging merge would not be adequate as two datasets were processed using different workflows and imaging was done using different velocity models. In order to synchronize the two datasets, we have processed them in parallel. Data matching and merging were done before regularization. It has been regularized to radial geometry using 5D Matching Pursuit with Fourier Interpolation (MPFI). This has provided 12 well sampled azimuth sectors that went through surface consistent processing, multiple attenuation, and residual noise attenuation. Near surface model was built using data-driven image-based static (DIBS) while reflection tomography was used to build the anisotropic velocity model. Imaging was done using Pre-Stack Kirchhoff Depth Migration. Processing legacy survey from the beginning has helped to improve signal to noise ratio which assisted with data merging to not degrade the quality of the end image. Building one near surface model allowed both datasets to match well in time domain. Bringing datasets to the same level was an important condition before matching and merging. Amplitude and phase analysis have shown that both surveys are aligned quite well with minimal difference. Only the portion of the legacy survey that covers the gap was used in the regularization, allowing MPFI to reconstruct missing data. Regularized data went through surface multiple attenuation and further noise attenuation as preconditioning for migration. Final image that is created using both datasets has allowed target to be imaged better.