Three key factors in seismic data reconstruction based on compressive sensing
Wen Rui1, Liu Guochang1, Ran Yang2
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum(Beijing), Beijing 102249, China; 2. Daqing Logging and Testing Service Company, Daqing Oilfield Company, PetroChina, Daqing, Heilongjiang 163000, China
Abstract：Seismic data reconstruction is significant for seismic data processing and imaging.Reconstruction methods based on compressive sensing are widely used.In these methods,the sparsity transform,the iterative algorithm,and the threshold model affect the final reconstruction performance and computation efficiency.For the sparsity transform,we analyze the influence of Fourier transform,Curvelet transform and Seislet transform in seismic data reconstruction.For the iterative algorithm,we discuss the projection onto convex sets (POCS),the iterative hard thresholding (IHT),Bregman,and the joint reconstruction by sparsity-promoting inversion (JRSI) reconstruction methods and analyze the advantages and disadvantages of these four methods.For the threshold model,we work on the linear threshold model,the exponential threshold model,and the data-driven threshold model.We analyze how these three key factors affect the reconstruction results through synthetic and real data examples.At the end we draw some conclusions and propose suggestions for practical seismic data reconstruction.
温睿, 刘国昌, 冉扬. 压缩感知地震数据重建中的三个关键因素分析[J]. 石油地球物理勘探, 2018, 53(4): 682-693.
Wen Rui, Liu Guochang, Ran Yang. Three key factors in seismic data reconstruction based on compressive sensing. Oil Geophysical Prospecting, 2018, 53(4): 682-693.
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