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CUP -- TABLE OF CONTENTS
Random noise attenuation using - 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 (-) domain. The method adaptively predicts the
signal with spatial changes in dip or amplitude using - RNA. The
key idea is to overcome the assumption of linearity and stationarity
of the signal in conventional - domain prediction technique. The
conventional - 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 - domain. The shaping regularization
in least square inversion controls the smoothness of the coefficients
of - 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 - domain and
time-space (-) domain prediction methods, - RNA can be more
effective in suppressing random noise and preserving the signals,
especially for complex geological structure.
Noncausal -- 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
-
-
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
-
-
NRNA uses more information from circumjacent
traces than 2D
-
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
-
NRNA method,
-
-
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.
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2013-11-13