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![]() | Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | ![]() |
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A benchmark example created by Raymond Abma (personal communication)
shows a simple curved event
(Figure 2a). The challenge in this
example is to account for both nonstationarity and
aliasing. Figure 2b shows the
interpolated result using Claerbout's stationary
-
PEF, which
was estimated and applied in one big window, with each PEF coefficient
constant at every data location. Note that the
-
PEF
method can recover the aliasing trace only in the dominant slope
range. The trace-interpolating result using regularized nonstationary
autoregression is shown in Figure 2c. The
adaptive PEF has 20 (time)
3 (space) coefficients for each
sample and a 20-sample (time)
3-sample (space) smoothing
radius. The proposed method eliminates all nonstationary aliasing and
improves the continuity of the curved event.
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jcurve,jcscov,jcacov
Figure 2. Curve model (a), trace interpolation with stationary PEF (b), and trace interpolation with adaptive PEF (c). |
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Abma and Kabir (2005) present a comparison of several algorithms used for
trace interpolation. We chose the most challenging benchmark Marmousi
example from Abma and Kabir to illustrate the performance of RNA
interpolation. Figure 3a shows a zero-offset
section of the Marmousi model, in which curved events violate the
assumptions common for most trace-interpolating
methods. Figure 3b shows that our method produces
reasonable results for both curved and weak events and does not
introduce any undesirable noise. The adaptive PEF parameters
correspond to 7 (time)
5 (space) coefficients for each sample
and a 40-sample (time)
30-sample (space) smoothing radius.
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jmarm,jmacov
Figure 3. Marmousi model (a) and trace interpolation with regularized nonstationary autoregression (b). |
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![]() | Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | ![]() |
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