Complex field conditions always create different interferences during seismic data acquisition,
and there exist several types of noise in the recorded data, which affect the subsequent data
processing and interpretation. To separate an effective signal from the noisy data, we adopted
a pattern-based method with a two-step strategy, which involves two adaptive prediction-error
filters (APEFs) corresponding to a nonstationary data pattern and noise pattern. By introducing
shaping regularization, we first constructed a least-squares problem to estimate the filter
coefficients of the APEF. Then, we solved another constrained least-square problem corresponding
to the pattern-based signal-noise separation, and different pattern operators are adopted to
characterize random noise and ground-roll noise. In comparison with traditional denoising methods,
such as FXDECON, curvelet transform and local time-frequency (LTF) decomposition, we examined
the ability of the proposed method by removing seismic random noise and ground-roll noise in
several examples. Synthetic models and field data demonstrate the validity of the strategy
for separating nonstationary signal and noise with different patterns.
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, Yang Liu
, Cai Liu
College of Geo-exploration Science and Technology,