Enhancing Massive Land 3D Seismic Data Using Nonlinear Beamforming: Performance, Quality and Practical Trade-Offs

Summary Modern land seismic data are typically acquired using high spatial trace density but single sensors or small source and receiver arrays. These datasets are challenging to process due to their massive size and rather low signal-to-noise ratio caused by scattered near surface noise. Prestack data enhancement becomes a critical step in processing flow. Nonlinear beamforming was proven very powerful for 3D land data. It requires computationally costly estimations of local coherency on dense spatial/temporal grids in 3D prestack data cubes and poses inevitable trade-off between performance of the algorithm and quality of the obtained results. In this work, we study different optimization schemes and discuss practical details required for applications of the algorithm to modern 3D land datasets with hundreds of terabytes of data.