![[pdf]](icons/pdf.png){kind=icon}
|
|
|
|
| OC-seislet: seislet transform construction with differential offset continuation | |
|
Yang Liu
and 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
Many of the geophysical data analysis problems, such as signal-noise separation and data regularization, are conveniently formulated in a transform domain, where the signal appears sparse. Classic transforms such as the Fourier transform or the digital wavelet transform, fail occasionally in processing complex seismic wavefields, because of the nonstationarity of seismic data in both time and space dimensions. We present a sparse multiscale transform domain specifically tailored to seismic reflection data. The new wavelet-like transform - the OC-seislet transform - uses a differential offset-continuation (OC) operator that predicts prestack reflection data in offset, midpoint, and time coordinates. It provides high compression of reflection events. In the transform domain, reflection events get concentrated at small scales. Its compression properties indicate the potential of OC-seislets for applications such as seismic data regularization or noise attenuation. Results of applying the method to both synthetic and field data examples demonstrate that the OC-seislet transform can reconstruct missing seismic data and eliminate random noise even in structurally complex areas.
|
|
|
|
| OC-seislet: seislet transform construction with differential offset continuation | |
|