We have introduced a new approach to adaptive prediction filter (APF)
for seismic random noise attenuation in
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domain. Our
approach uses regularized nonstationary autoregression (RNA) to handle
time-space variation of nonstationary seismic data. These properties
are useful for application such as random noise attenuation. The
predicted signal provides a noise-free estimation of local plane
events. Compared with the
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NRNA method,
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APF can
capture more detailed signal and avoid most artifacts, which occur
more in frequency domain methods. However, the
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NRNA method
uses fewer prediction coefficients (no time prediction) to save
storage space and can be applied in parallel to different frequency
slices. Therefore,
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NRNA is appropriate for mild complex
structure and fast computation while
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APF is more
appropriate for very complex structures. Experiments with synthetic
examples and field data tests show that the proposed filters are able
to depict variations in the nonstationary signal and provide a
accurate estimation of complex wavefields even in the presence of
strongly curved and conflicting events.
Adaptive prediction filtering in
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domain for random noise attenuation using regularized nonstationary autoregression