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Seismic Data Reconstruction Combining Discrete Cosine Transform and Shearlet Transform
HAIYANG YAN1,2,3,4 ,
HUI ZHOU1,2,3 ,
HAIBO LIU4 ,
ZHAOHONG XU4 ,
SUN ZANDONG4 ,
LIU ZHAO4 ,
ZHANG MINGKUN1,2,3
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1
State Key Laboratory of Petroleum Resources and Prospecting, Beijing 100029, f.R. China.,
2
CNPC Key Lab of Geophysical Exploration, Beijing, 100029, P.R. China,
3
China University of Petroleum-Beijing, Beijing 102249, P.R. China,
4
BGP Offshore, CNPC, Tianjin 300457, P.R. China,
JSE 2023, 32(4), 301–314;
Submitted: 1 February 2023 | Accepted: 6 May 2023 | Published: 1 August 2023
© 2023 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
The irregularity of seismic data caused by field acquisition affects the imaging quality of subsequent seismic data processing. The reconstruction method based on compressed sensing theory can effectively restore seismic data. The aliasing caused by randomly missing seismic traces is distributed as white noise, and the effective signals are concentrated in the sparse domain. This paper transforms the seismic data reconstruction in the t-x domain into the random noise suppression problem in the discrete cosine transform (DCT) domain. The DCT is a global transform, which transforms the discontinuous t-x data into the continuous DCT data. We do multi-scale directional shearlet transform on the data in the DCT domain and eliminate the aliasing in the DCT domain through iterative inversion. The shearlet transform after the DCT can be used as a new sparse basis transform. The reconstruction experiments show that the reconstruction accuracy in the DCT+shearlet domain is higher than that in the shearlet domain.
Keywords
shearlet transform
DCT
compressed sensing
seismic data reconstruction.
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