基于压缩感知的地震数据规则化技术在塔里木区块的应用

doi:10.11720/wtyht.2025.2581

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Abstract

The Tarim Basin,one of China's most significant oil exploration areas,exhibits thick sedimentary rock layers and frequent tectonic movements.These characteristics have led to the formation of abundant source,reservoir,and cap rocks,creating favorable conditions for the generation and storage of resources like petroleum.However,the high topographic relief in the area poses significant challenges to observation system arrangement and acquisition engineering design.Moreover,undulating surfaces and complex subsurface structures affect the propagation of seismic waves,impairing the quality of seismic exploration data and complicating data preprocessing,imaging,and reservoir prediction.Given the data loss of the Tarim block caused by suboptimal data collection,this study conducted high-precision reconstruction of seismic data using the compressed sensing technique,aiming to provide seismic records with high integrity,reliability,and precision for the preprocessing/superimposition phase.Compressed sensing,a novel sampling technique,plays a significant role in data reconstruction.The key to this technique is the adequate sparse representation of seismic data.However,conventional transform methods like Fourier transform and discrete cosine transform(DCT) are merely applicable to simple global structures.Considering the high complexity of the Tarim block data,this study employed the Shearlet transform as the sparse basis function for data reconstruction through compressed sensing.The technology of this study was finally applied to process the actual data of the Tarim Basin,demonstrating high accuracy and applicability for the area.

Key words： seismic data processing seismic data reconstruction compressed sensing sparse transform sparse inversion

Received: 29 December 2023 Published: 26 February 2025

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	Articles by authors
	Duo-Ming ZHENG
	De-Ying WANG
	Yu-Bing WU
	Long-Jiang KOU
	Yang-Yang CHEN
	Bao-Zhong JIN

Cite this article:

Duo-Ming ZHENG,De-Ying WANG,Yu-Bing WU, et al. Application of compressed sensing-based seismic data regularization technology in the Tarim block[J]. Geophysical and Geochemical Exploration, 2025, 49(1): 189-199.

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https://www.wutanyuhuatan.com/EN/10.11720/wtyht.2025.2581 OR https://www.wutanyuhuatan.com/EN/Y2025/V49/I1/189

Impact of complex near-surface velocities on imaging
blue line—surface;orange,yellow,purple line—structural layers;red line—small-scale structures such as beads and cavities;black line—schematic of seismic wave propagation paths

Characteristics of five sparse representation methods

Random undersampling of synthetic seismic data
a—synthetic seismic data;b—0% random undersampling matrix;c—0% missing data

Reconstruction results of four single transformations
a—reconstruction result based on Fourier transform;b—reconstruction result based on DCT transform;c—reconstruction result based on Curvelet transform;d—reconstruction result based on Shearlet transform

Reconstruction error of four single transformations
a—reconstruction error based on Fourier transform;b—reconstruction error based on DCT transform;c—reconstruction error based on Curvelet transform;d—reconstruction error based on Shearlet transform

Spectrum before and after Shearlet reconstruction
a—spectrum of seismic records with 50% random missing data;b—spectrum of Shearlet reconstructed data

Random undersampling of 3D synthetic seismic data
a—3D synthetic seismic data;b—50% random sampling matrix;c—50% missing data

Reconstruction results of three single transformations
a—reconstruction result based on Fourier transform;b—reconstruction result based on DCT transform;c—reconstruction result based on Shearlet transform

Reconstruction error of three single transformations
a—reconstruction error based on Fourier transform;b—reconstruction error based on DCT transform;c—reconstruction error based on Shearlet transform

Real single shot

Missing single shot
a—seismic records with 20% random missing data;b—seismic records with 40% random missing sata;c—seismic records with 60% random missing data;d—seismic records with 80% random missing data

Reconstructed single shot
a—reconstructed seismic records with 20% random missing data(SNR=11.5 dB;PSNR=2.7 dB);b—reconstructed seismic records with 40% random missing sata(SNR=10.43 dB;PSNR=1.2 dB);c—reconstructed seismic records with 60% random missing data(SNR=8.93 dB;PSNR=0.89 dB);d—reconstructed seismic records with 80% random missing data(SNR=8.36 dB;PSNR=0.78 dB)

Error of reconstruction
a—reconstruction error with 20% random missing data;b—reconstruction error with 40% random missing data;c—reconstruction error with 60% random missing data;d—reconstruction error with 80% random missing data

Comparison of original shot records before and after reconstruction
a—original single shot(SNR=5.39 dB;PSNR=0.2 dB);b—reconstructed single shot(SNR=7.53 dB;PSNR=1.6 dB)

Stacking comparison before and after reconstruction
a—original data stack with local magnification 1;b—reconstructed data stack with local magnification 1;c—original data stack with local magnification 2;d—reconstructed data stack with local magnification 2

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