Seismic data interpolation beyond aliasing using regularized nonstationary autoregression |

sean,sean2
A 2-D marine shot
gather. Original input (a) and input subsampled by a
factor of 2 (b).
Figure 5. |
---|

samiss,serr
Shot gather
after trace interpolation (adaptive PEF with 15
5) (a) and difference between original
gather (Figure 5a) and
interpolated result (Figure 6a)
(b).
Figure 6. |
---|

win,win2
Close-up comparison
of original data (a) and interpolated result by
RNA (b).
Figure 7. |
---|

sfcoe,smcoe
Adaptive PEF
coefficients. First coefficient
(a) and mean
coefficient of
(b).
Figure 8. |
---|

For a missing-trace interpolation test (Figure 9a), we removed 40% of randomly selected traces from the input data (Figure 5a). Furthermore, the first five traces were also removed to simulate traces missing at near offset. The adaptive PEF can only use a small number of coefficients in the spatial direction because of a small number of fitting equations (where the adaptive PEF lies entirely on known data). However, it also limits the ability of the proposed method to interpolate dipping events. We used a nonstationary PEF with 4 (time) 3 (space) coefficients for each sample and a 50-sample (time) 10-sample (space) smoothing radius to handle the missing trace recovery. The result is shown in Figure 9b. By comparing the results with the original input (Figure 5a), the missing traces are interpolated reasonably well except for weaker amplitude of the steeply dipping events.

zero,ramiss
Field data
with 40% randomly missing traces (a), and
reconstructed data using RNA (b).
Figure 9. |
---|

An extension of the method to 3-D is straightforward and follows a two-step least-squares method with 3-D adaptive PEF estimation. We use a set of shot gathers as the input data volume to further test our method (Figure 10a). We removed 50% of randomly selected traces and five near offset traces for all shots (Figure 10b). For comparison, we used PWD to recover the missing traces (Figure 11a). The PWD method produces a reasonable result after carefully estimating dip information, but the interpolated error is slightly larger in the diffraction locations. (Figure 11b). The additional direction provided more information for interpolation but also increased the number of zeros in the mask operator , which constrains enough fitting equations in equation 13. To use the available fitting equations for adaptive PEF estimation, we chose a smaller number of coefficients in the spatial direction. The proposed method is able to handle conflicting dips, although it does not appear to improve the dipping-event recovery compared to the 2-D case. This characteristic partly limits the application of RNA in 3-D case. We used a 3-D nonstationary PEF with 4 (time) 2 (space) 2 (space) coefficients for each sample and a 50-sample (time) 10-sample (space) 10-sample (space) smoothing radius was selected. Similar to the result in the 2-D example, Figure 11c shows the interpolation result, in which only steeply-dipping low-amplitude diffraction events with are lost (Figure 11d).

sean3,zero3
A
3-D field data volume (a) and data with
50% randomly missing traces (b).
Figure 10. |
---|

miss3,diff3,amiss3,adiff3
Reconstructed data volume using 3-D plane-wave
destruction (a), difference between original input
(Figure 10a) and interpolated
result (Figure 11a),
reconstructed data volume using 3-D regularized
nonstationary autogression (c), and difference between
original input (Figure 10a) and
interpolated result
(Figure 11c) (d).
Figure 11. |
---|

Seismic data interpolation beyond aliasing using regularized nonstationary autoregression |

2013-07-26