3-D seismic survey design provides an acquisition geometry for obtaining seismic data that enable imaging and amplitude-versus-offset applications of target reflectors with sufficient quality under given economical and operational constraints. However, in land or shallow water environments, surface waves are often dominant and will lower the quality of the final output. The necessity to remove them from the seismic data imposes additional constraints on the acquisition parameters. Here, we try to understand how the application of surface-wave separation affects the choice of survey parameters and the resulting data quality. Quality is quantified by the signal-to-noise ratio of the seismic data after surface-wave separation or removal. In a case study, we applied surface-wave separation and signal-to-noise ratio estimation to several data sets with different survey parameters. We found that the spatial sampling intervals of the basic subset are the most important ones among the various types of survey parameters. The resulting data quality as a function of the spatial sampling intervals follows a trend curve. Finer spatial sampling intervals lead to better data quality up to a point where the curve flattens and a plateau is reached. The actual shape of the trend curve depends on the method of surface-wave separation.