Noncausal f-x-y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data
Liu, Guochang*; Chen, Xiaohong
Seismic noise attenuation is very important for seismic data analysis and interpretation, especially for 3D seismic data. In this paper, we propose a novel method for 3D seismic random noise attenuation by applying noncausal regularized nonstationary autoregression (NRNA) in f-x-y domain. The proposed method, 3D NRNA (f-x-y domain) is the extended version of 2D NRNA (f-x domain). f-x-y NRNA can adaptively estimate seismic events of which slopes vary in 3D space. The key idea of this paper is to consider that the central trace can be predicted by all around this trace from all directions in 3D seismic cube, while the 2D f-x NRNA just considers that the middle trace can be predicted by adjacent traces along one space direction. 3D f-x-y NRNA uses more information from circumjacent traces than 2D f-x NRNA to estimate signals. Shaping regularization technology guarantees that the nonstationary autoregression problem can be realizable in mathematics with high computational efficiency. Synthetic and field data examples demonstrate that, compared with f-x NRNA method, f-x-y NRNA can be more effective in suppressing random noise and improve trace-by-trace consistency, which are useful in conjunction with interactive interpretation and auto-picking tools such as automatic event tracking.