Seismic data reconstruction by SR-ADMM algorithm based on compressed sensing
DUAN Zhongyu1, LI Tingting1, XIAO Yong1, WANG Yunlei2, ZHENG Guijuan3
1. School of Information & Communication Engineering, Beijing Information Science & Technology University, Beijing, 100101, China; 2. BGP Intl., CNPC, Zhuozhou, Hebei 072751, China; 3. Geophysical Research Institute, BGP, CNPC, Zhuozhou, Hebei 072751, China
Abstract:The lack of seismic data due to the field environment and operation cost will affect subsequent processing and interpretation of seismic data. Thus, it is of great significance to reconstruct the missing seismic data. In light of the compressed sensing theory, complete data can be recovered by an optimization algorithm at a frequency lower than the Nyquist sampling frequency on the pre-mise of sparse data. In this paper, a square-regular alternating direction method of multipliers (SR-ADMM) based on compressed sensing is proposed for seismic data reconstruction. The square regularization term is added to the SR-ADMM algorithm in the iterative process of the alternating direction method of multipliers, and this algorithm realizes the adaptive selection of the parameter ba-lance factor. First, sparse representation of missing seismic data is made by dictionary learning, and then the SR-ADMM algorithm is used to solve the optimization problem and reconstruct the missing seismic data. The reconstruction of simulated seismic data and actual data of Daqing Oilfield shows that the SR-ADMM algorithm proposed in this paper has high reconstruction accuracy and certain practicability.
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