Sparse Deconvolution Based on the Curvelet Transform
详细信息
- 责任者：Meng Dajiang (College of Geoexploration Science & Technology ; Jilin University ; Changchun 130026 ; China) ; Wang Deli
- 刊名：Acta Petrolei Sinica
- 出版年：2013
- 卷期：34(1)
- 关键词：地震勘探 ; seismic exploration ; 反褶积 ; deconvolution
- 页码：p.107-114
- 图表：illus., 23 refs.
- 分类号：1600
- issnNo：ISSN0253-2697
- isbnNo：CN11-2128/TE
摘要
Traditional deconvolution methods usually need to assume a sparse distribution for seismic reflectivity,and then apply the L1 norm deconvolution to get sparse reflectivity so as to improve resolution,but this doesn’t conform to reality.In addition,when traditional methods improve the resolution,they reduce the signal to noise ratio at the same time,making the continuity of a seismic profile poor.In view of these problems,the sparse deconvolution based on the Curvelet transform was proposed in the present paper.The Curvelet transform is characterized by an optimum sparseness expression for multidimensional signals to have the best nonlinear approximation,thus it can be used to express seismic reflectivity.When the Curvelet transform was introduced to the L1 norm deconvolution,a sparse Curvelet coefficient representing reflectivity could be obtained without assuming the sparseness of reflectivity.In addition,according to the distribution characteristics of effective signals and noise the signal to noise ratio could be improved by using a threshold method to suppress noise,and consequently the multidimensional seismic deconvolution was obtained to maintain the continuity of seismic profiles.Finally,a threshold iterative algorithm was proposed to solve the L1 norm deconvolution problem.The results show that this proposed method can effectively improve resolution and continuity of seismic profiles while suppressing random noise.