Abstract
Irregular seismic data causes problems with multi-trace processing algorithms and degrades processing quality. We introduce the Projection onto Convex Sets (POCS) based image restoration method into the seismic data reconstruction field to interpolate irregularly missing traces. For entire dead traces, we transfer the POCS iteration reconstruction process from the time to frequency domain to save computational cost because forward and reverse Fourier time transforms are not needed. In each iteration, the selection threshold parameter is important for reconstruction efficiency. In this paper, we designed two types of threshold models to reconstruct irregularly missing seismic data. The experimental results show that an exponential threshold can greatly reduce iterations and improve reconstruction efficiency compared to a linear threshold for the same reconstruction result. We also analyze the antinoise and anti-alias ability of the POCS reconstruction method. Finally, theoretical model tests and real data examples indicate that the proposed method is efficient and applicable.
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This work is financially supported by National 863 Program (Grants No.2006AA 09A 102-09) and National Science and Technology of Major Projects (Grants No.2008ZX0 5025-001-001).
Gao Jian-Jun is a PhD student at China University of Petroleum (Beijing). He graduated from the China University of Petroleum (East China) in 2006. He is mainly engaged in the field of digital seismic data processing, especially on the research of irregular seismic data interpolation and regularization.
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Gao, JJ., Chen, XH., Li, JY. et al. Irregular seismic data reconstruction based on exponential threshold model of POCS method. Appl. Geophys. 7, 229–238 (2010). https://doi.org/10.1007/s11770-010-0246-5
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DOI: https://doi.org/10.1007/s11770-010-0246-5