Seismic data decomposition and reconstruction with sparse Gaussian beams and sparse optimisation method
Peng Liu 1 Yanfei Wang 1 2
+ Author Affiliations
- Author Affiliations
1 Key Laboratory of Petroleum Resources Research, Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing 100029, China.
2 Corresponding author. Email: yfwang@mail.iggcas.ac.cn
Exploration Geophysics 49(1) 50-57 https://doi.org/10.1071/EG15114
Submitted: 8 April 2015 Accepted: 26 August 2016 Published: 25 October 2016
Abstract
We study seismic data decomposition and reconstruction problems in this paper. Seismic data representation using sparse Gaussian beams is proposed. We formulate this problem as an l0-norm constrained minimisation problem. In solving the l0-norm minimisation, dip-angle scanning is performed and a quasi-Newton method is utilised to calculate the waveform function. Numerical experiments on synthetic data and real data indicate that the seismic data can be properly represented using sparse Gaussian beams with the sparse optimisation method. Moreover, the method has the ability to remove random noise and recover missing data. Finally, the waveform function obtained by sparse decomposition can be used for Gaussian beam migration. The approach described here can obtain a higher signal-to-noise ratio image than the traditional poststack Gaussian beam migration, and the overall runtime is comparable.
Key words: Gaussian beams, optimisation, sparse decomposition.
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