CUP -- TABLE OF CONTENTS

CUP -- TABLE OF CONTENTS

Random noise attenuation using $f$-$x$ regularized nonstationary autoregression [pdf 13M]
Guochang Liu, Xiaohong Chen, Jing Du, and Kailong Wu
We propose a novel method for random noise attenuation in seismic data by applying regularized nonstationary autoregression (RNA) in frequency-space ($f$-$x$) domain. The method adaptively predicts the signal with spatial changes in dip or amplitude using $f$-$x$ RNA. The key idea is to overcome the assumption of linearity and stationarity of the signal in conventional $f$-$x$ domain prediction technique. The conventional $f$-$x$ domain prediction technique uses short temporal and spatial analysis windows to cope with the nonstationary of the seismic data. The proposed method does not require windowing strategies in spatial direction. We implement the algorithm by iterated scheme using conjugate gradient method. We constrain the coefficients of nonstationary autoregression (NA) to be smooth along space and frequency in $f$-$x$ domain. The shaping regularization in least square inversion controls the smoothness of the coefficients of $f$-$x$ RNA. There are two key parameters in the proposed method: filter length and radius of shaping operator. Synthetic and field data examples demonstrate that, compared with $f$-$x$ domain and time-space ($t$-$x$) domain prediction methods, $f$-$x$ RNA can be more effective in suppressing random noise and preserving the signals, especially for complex geological structure.
Noncausal $f$-$x$-$y$ regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data [pdf 1.8M]
Guochang Liu and Xiaohong Chen
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 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 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.

CUP -- TABLE OF CONTENTS