Efficient anti-aliasing and anti-leakage Fourier transform for high-dimensional seismic data regularization using cube removal and GPU

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外文摘要：Seismic data is commonly acquired sparsely and irregularly,which necessitates the regularization of seismic data with anti-aliasing and anti-leakage methods during seismic data processing.We propose a novel method of 4D anti-aliasing and anti-leakage Fourier transform using a cube-removal strategy to address the combination of irregular sampling and aliasing in high-dimensional seismic data.We compute a weighting function by stacking the spectrum along the radial lines,apply this function to suppress the aliasing energy,and then iteratively pick the dominant amplitude cube to construct the Fourier spectrum.The proposed method is very efficient due to a cube removal strategy for accelerating the convergence of Fourier reconstruction and a well-designed parallel architecture using CPU/GPU collaborative computing.To better fill the acquisition holes from 5D seismic data and meanwhile considering the GPU memory limitation,we developed the anti-aliasing and anti-leakage Fourier transform method in 4D with the remaining spatial dimension looped.The entire workflow is composed of three steps:data splitting,4D regularization,and data merging.Numerical tests on both synthetic and field data examples demonstrate the high efficiency and effectiveness of our approach.

外文关键词：

High-dimensional regularizationGPUAnti-aliasingAnti-leakage

作者：

Lu Liu、Sindi Ghada、Fu-Hao Qin、Youngseo Kim、Vladimir Aleksic、Hong-Wei Liu

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作者单位：

Aramco Research Center-Beijing,Aramco Asia,Beijing,100102,China

KAUST Research Center Group,Saudi Aramco,Thuwal,23955-6900,Saudi Arabia

EXPEC Advanced Research Center,Saudi Aramco,Dhahran,31311,Saudi Arabia

Geophysical Imaging Department,Saudi Aramco,Dhahran,31311,Saudi Arabia

National Key Laboratory of Deep Oil and Gas,China University of Petroleum(East China),Qingdao 266580,Shandong,China

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出版年：

2024

DOI：

10.1016/j.petsci.2024.04.002

石油科学(英文版)

中国石油大学（北京）

石油科学(英文版)

影响因子：0.88

ISSN：1672-5107

年,卷(期)：2024.21(5)