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![]() | Seismic data interpolation using streaming prediction filter in the frequency domain | ![]() |
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We introduced a fast approach to adaptive PF for missing data
interpolation in the frequency domain. Instead of using the iterative
optimization algorithm, we proposed a two-step interpolation strategy
with noniterative SPF in the
-
and
-
-
domains. The
proposed method employs a local and multidimensional similarity to
constrain the autoregression equations for adaptive PFs in the
frequency domain, which are based on the streaming computation
framework. The SPF in the frequency domain provides a fast and
reasonably accurate estimation of nonstationary seismic data. To
guarantee the interpolation results, we also designed the filter
structure and the processing path according to the characteristics of
the interpolation problem. The synthetic and field examples show that
the proposed SPF in the frequency domain can depict nonstationary
signal variation and provide a reliable description of complex
wavefield with low computational cost even when analyzing large-scale
seismic data. The properties are suitable for missing data
interpolation in practice. Finally, we discussed the problems of
parameter selection, interpolation of regularly decimated data, and
interpolation of low SNR data; the proposed methods can cope with such
problems.
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![]() | Seismic data interpolation using streaming prediction filter in the frequency domain | ![]() |
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