Compressive Sensing Acquisition with Application to Marchenko Imaging

Marchenko-based method is a novel multiple-free imaging approach for a target subsurface area. High-density acquisition can help obtain an accurate Marchenko imaging, but will significantly increase the acquisition cost. Obtaining accurate Marchenko imaging while maintaining low acquisition cost is therefore an important practical hurdle to overcome. I develop a compressive sensing-based low-cost acquisition design relying on a sparse and random irregular survey to meet the needs of the Marchenko imaging. I examine the influence of noise on Marchenko imaging, and demonstrate that reconstructing seismic data from noisy observations by using compressive sensing has a natural de-noising effect, and yield comparable Green’s functions and resultant high-quality angle gathers and Marchenko imaging. Numerical tests using a 2D model show that as few as 30% of receivers are needed when compressive-sensing reconstruction is combined with Marchenko method.