Zhang, M., 2023. Efficient 3D seismic acquisition design using compressive sensing principles. Journal of Seismic Exploration, 32: 427-454. The ability to acquire 3D seismic data efficiently and cost-effectively is a major consideration in many applications. One way to achieve this goal is through the theory of compressive sensing. Compressive sensing uses sparse acquisition designs combined with the post-acquisition reconstruction to reduce the number of sensors. Sensor deployment or sampling pattern is a critical component in compressive sensing. Therefore, we analyze sampling patterns based on a Spectral Resolution Function (SRF) to improve the quality of acquired data. We have investigated two types of sparse seismic acquisition designs that use fewer receivers deployed irregularly, and also have compared three proposed reconstruction methods for each acquisition design. We predict the reconstruction accuracies of these six strategies, and then we verify our prediction using SEAM seismic dataset. SEAM seismic data examples demonstrate three major results: First, irregular line and irregular point patterns have different properties of SRF, and these properties can be applied to improve the accuracy of compressive sensing results. Second, a good combination of acquisition design and post-acquisition reconstruction selected based on the properties of SRF is able to obtain better reconstructed shot gathers and imaging results. Third, numerical simulations show that we can reconstruct single shot gather using only 25% of receivers and then produce seismic migration images comparable to those obtained from the full shot gathers. The overall results indicate that, the combination of a sparse acquisition design and corresponding compressive sensing reconstruction method could help facilitate a new generation of cost-effective seismic acquisitions.