1887
Volume 49, Issue 1
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

[

Sparse Gaussian beam decomposition and reconstruction is an ill-posed inverse problem. We construct a zero-norm constrained minimisation model, develop a dip-angle scanning strategy and adopt a quasi-Newton method to find the solution. This method can recover full wave field data and can be used for sparse Gaussian beam migration.

,

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 -norm constrained minimisation problem. In solving the -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.

]
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2018-02-01
2026-01-13

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/content/journals/10.1071/EG15114
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Seismic data decomposition and reconstruction with sparse Gaussian beams and sparse optimisation method
Exploration Geophysics 49, 50 (2018); https://doi.org/10.1071/EG15114
/content/journals/10.1071/EG15114
/content/journals/10.1071/EG15114
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  • Article Type: Research Article
Keyword(s): Gaussian beams; optimisation; sparse decomposition

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