Estimating the spatial distribution of coseismic slip is an ill-posed inverse problem, and solutions may be extremely oscillatory due to measurement errors without any constraints on the coseismic slip distribution. I...Estimating the spatial distribution of coseismic slip is an ill-posed inverse problem, and solutions may be extremely oscillatory due to measurement errors without any constraints on the coseismic slip distribution. In order to obtain stable solution for coseismic slip inversion, regularization method with smoothness-constrained was imposed. Trade-off parameter in regularized inversion, which balances the minimization of the data misfit and model roughness, should be a critical procedure to achieve both resolution and stability. Then, the active constraint balancing approach is adopted, in which the trade-off parameter is regarded as a spatial variable at each model parameter and automatically determined via the model resolution matrix and the spread function. Numerical experiments for a synthetical model indicate that regularized inversion using active constraint balancing approach can provides stable inversion results and have low sensitivity to the knowledge of the exact character of the Gaussian noise. Regularized inversion combined with active constraint balancing approach is conducted on the 2005 Nias earthquake. The released moment based on the estimated coseismic slip distribution is 9.91×1021 N·m, which is equivalent to a moment magnitude of 8.6 and almost identical to the value determined by USGS. The inversion results for synthetic coseismic uniform-slip model and the 2005 earthquake show that smoothness-constrained regularized inversion method combined with active constraint balancing approach is effective, and can be reasonable to reconstruct coseismic slip distribution on fault.展开更多
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....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 Ll-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 Ll-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.展开更多
基金Projects(41604111,41541036) supported by the National Natural Science Foundation of China
文摘Estimating the spatial distribution of coseismic slip is an ill-posed inverse problem, and solutions may be extremely oscillatory due to measurement errors without any constraints on the coseismic slip distribution. In order to obtain stable solution for coseismic slip inversion, regularization method with smoothness-constrained was imposed. Trade-off parameter in regularized inversion, which balances the minimization of the data misfit and model roughness, should be a critical procedure to achieve both resolution and stability. Then, the active constraint balancing approach is adopted, in which the trade-off parameter is regarded as a spatial variable at each model parameter and automatically determined via the model resolution matrix and the spread function. Numerical experiments for a synthetical model indicate that regularized inversion using active constraint balancing approach can provides stable inversion results and have low sensitivity to the knowledge of the exact character of the Gaussian noise. Regularized inversion combined with active constraint balancing approach is conducted on the 2005 Nias earthquake. The released moment based on the estimated coseismic slip distribution is 9.91×1021 N·m, which is equivalent to a moment magnitude of 8.6 and almost identical to the value determined by USGS. The inversion results for synthetic coseismic uniform-slip model and the 2005 earthquake show that smoothness-constrained regularized inversion method combined with active constraint balancing approach is effective, and can be reasonable to reconstruct coseismic slip distribution on fault.
基金Projects(U1562215,41674130,41404088)supported by the National Natural Science Foundation of ChinaProjects(2013CB228604,2014CB239201)supported by the National Basic Research Program of China+1 种基金Projects(2016ZX05027004-001,2016ZX05002006-009)supported by the National Oil and Gas Major Projects of ChinaProject(15CX08002A)supported by the Fundamental Research Funds for the Central Universities,China
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 Ll-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 Ll-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.
