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JLU -- TABLE OF CONTENTS

Adaptive prediction filtering in $t$-$x$-$y$ domain for random noise attenuation using regularized nonstationary autoregression [pdf 4.9M]
Yang Liu, Ning Liu, and Cai Liu
Many natural phenomena, including geologic events and geophysical data, are fundamentally nonstationary. They may exhibit stationarity on a short timescale but eventually alter their behavior in time and space. We propose a 2D $ t$ -$ x$ adaptive prediction filter (APF) and further extend this to a 3D $ t$ -$ x$ -$ y$ version for random noise attenuation based on regularized nonstationary autoregression (RNA). Instead of using patching, a popular method for handling nonstationarity, we obtain smoothly nonstationary APF coefficients by solving a global regularized least-squares problem. We use shaping regularization to control the smoothness of the coefficients of APF. 3D space-noncausal $ t$ -$ x$ -$ y$ APF uses neighboring traces around the target traces in the 3D seismic cube to predict noise-free signal, so it provides more accurate prediction results than the 2D version. In comparison with other denoising methods, such as frequency-space deconvolution, time-space prediction filter, and frequency-space RNA, we test the feasibility of our method in reducing seismic random noise on three synthetic datasets. Results of applying the proposed method to seismic field data demonstrate that nonstationary $ t$ -$ x$ -$ y$ APF is effective in practice.
Seismic dip estimation based on the two-dimensional Hilbert transform and its application in random noise attenuation [pdf 652K]
Cai Liu, Changle Chen, Dian Wang, Yang Liu, Shiyu Wang, and Liang Zhang
In seismic data processing, random noise seriously affects the seismic data quality and subsequently the interpretation. This study aims to increase the signal-to-noise ratio by suppressing random noise and improve the accuracy of seismic data interpretation without losing useful information. Hence, we propose a structure-oriented polynomial fitting filter. At the core of structure-oriented filtering is the characterization of the structural trend and the realization of nonstationary filtering. First, we analyze the relation of the frequency response between two-dimensional (2D) derivatives and the 2D Hilbert transform (Riesz transform). Then, we derive the noniterative seismic local dip operator using the 2D Hilbert transform to obtain the structural trend. Second, we select polynomial fitting as the nonstationary filtering method and expand the application range of the nonstationary polynomial fitting. Finally, we apply variableamplitude polynomial fitting along the direction of the dip to improve the adaptive structureoriented filtering. Model and field seismic data show that the proposed method suppresses the seismic noise while protecting structural information.




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2015-05-07