Abstract
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.
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Foundation item: Projects(U1562215, 41674130, 41404088) supported by the National Natural Science Foundation of China; Projects(2013CB228604, 2014CB239201) supported by the National Basic Research Program of China; Projects(2016ZX05027004-001, 2016ZX05002006-009) supported by the National Oil and Gas Major Projects of China; Project(15CX08002A) supported by the Fundamental Research Funds for the Central Universities, China
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Pan, Xp., Zhang, Gz., Song, Jj. et al. Robust elastic impedance inversion using L1-norm misfit function and constraint regularization. J. Cent. South Univ. 24, 227–235 (2017). https://doi.org/10.1007/s11771-017-3423-y
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DOI: https://doi.org/10.1007/s11771-017-3423-y