|
|
|
| Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | |
|
Next: Introduction
Up: Reproducible Documents
Published as Geophysics, 76, V69-V77, (2011)
Seismic data interpolation beyond aliasing using regularized nonstationary autoregression
Yang Liu, Sergey Fomel
College of Geo-exploration Science and Technology,
Jilin University
No.6 Xi minzhu street,
Changchun, China, 130026
Bureau of Economic Geology,
John A. and Katherine G. Jackson School of Geosciences
The University of Texas at Austin
University Station, Box X
Austin, TX, USA, 78713-8924
Abstract:
Seismic data are often inadequately or irregularly sampled along
spatial axes. Irregular sampling can produce
artifacts in seismic imaging results. We present a new approach to
interpolate aliased seismic data based on adaptive prediction-error
filtering (PEF) and regularized nonstationary autoregression. Instead
of cutting data into overlapping windows (patching), a popular method
for handling nonstationarity, we obtain smoothly nonstationary PEF
coefficients by solving a global regularized least-squares
problem. We employ shaping regularization to
control the smoothness of adaptive PEFs. Finding the interpolated
traces can be treated as another linear least-squares problem, which
solves for data values rather than filter coefficients. Compared with
existing methods, the advantages of the proposed method include an
intuitive selection of regularization parameters and fast
iteration convergence. Benchmark synthetic and
field data examples show that the proposed technique can successfully
reconstruct data with decimated or missing traces.
|
|
|
| Seismic data interpolation beyond aliasing using regularized nonstationary autoregression | |
|
Next: Introduction
Up: Reproducible Documents
2013-07-26