Three key factors in seismic data reconstruction based on compressive sensing

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. © 2018, Editorial Department OIL GEOPHYSICAL PROSPECTING. All right reserved.