Sparse deconvolution based on Curvelet transform of improved threshold
Yu Jiangqi1,2,3, Cao Siyuan1,2,3, Chen Hongling1,2,3, He Zhen1,2,3, Wang Zhiqiang1,2,3, Chen Shuqiao1,2,3
1. CNPC Key Laboratory of Geophysical Exploration, China Petroleum University (Beijing), Beijing 102249, China;
2. State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum (Beijing), Beijing 102249, China;
3. College of Geophysics and Information Engineering, China University of Petroleum (Beijing), Beijing 102249, China
Abstract:Since reflection coefficients in the Curvelet domain are rather sparse,we can achieve sparse deconvolution in the Curvelet domain,and at the same time use the spatial similarity of reflection coefficients to constraint seismic data inversion results. An improved and smooth threshold function is used for denoising,and parameters can reasonably be adjusted to flexibly change the threshold function between the hard threshold and the soft threshold. The improved threshold function is applied for sparse deconvolution in the Curvelet domain. Processing tests on both synthetic and real data show that the proposed approach performs well in the reflection coefficient inversion,has a certain resistance to noise,and can well condense wavelet to enhance seismic data resolution.
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