Robust elastic impedance inversion using L1-norm misfit function and constraint regularization

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• 作者：Xin-peng Pan 潘新朋 ; Guang-zhi Zhang 张广智…
• 关键词：Key wordselastic impedance (EI) inversion ; L1 ; norm misfit function ; sparse constraint regularization ; elastic impedance point constraint regularization ; IRLS strategy
• 刊名：Journal of Central South University
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：24
• 期：1
• 页码：227-235
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Engineering, general; Metallic Materials;
• 出版者：Central South University
• ISSN：2227-5223
• 卷排序：24

文摘

The classical elastic impedance (EI) inversion method, however, is based on the L2-norm misfit function and considerably sensitive to outliers, assuming the noise of the seismic data to be the Guassian-distribution. So we have developed a more robust elastic impedance inversion based on the L1-norm misfit function, and the noise is assumed to be non-Gaussian. Meanwhile, some regularization methods including the sparse constraint regularization and elastic impedance point constraint regularization are incorporated to improve the ill-posed characteristics of the seismic inversion problem. Firstly, we create the L1-norm misfit objective function of pre-stack inversion problem based on the Bayesian scheme within the sparse constraint regularization and elastic impedance point constraint regularization. And then, we obtain more robust elastic impedances of different angles which are less sensitive to outliers in seismic data by using the IRLS strategy. Finally, we extract the P-wave and S-wave velocity and density by using the more stable parameter extraction method. Tests on synthetic data show that the P-wave and S-wave velocity and density parameters are still estimated reasonable with moderate noise. A test on the real data set shows that compared to the results of the classical elastic impedance inversion method, the estimated results using the proposed method can get better lateral continuity and more distinct show of the gas, verifying the feasibility and stability of the method.