发明名称 |
UNCERTAINTY ESTIMATION FOR LARGE-SCALE NONLINEAR INVERSE PROBLEMS USING GEOMETRIC SAMPLING AND COVARIANCE-FREE MODEL COMPRESSION |
摘要 |
A method for uncertainty estimation for nonlinear inverse problems includes obtaining an inverse model of spatial distribution of a physical property of subsurface formations. A set of possible models of spatial distribution is obtained based on the measurements. A set of model parameters is obtained. The number of model parameters is reduced by covariance free compression transform. Upper and lower limits of a value of the physical property are mapped to orthogonalspace. A model polytope including a geometric region of feasible models is defined. At least one of random and geometric sampling of the model polytope is performed in a reduced-dimensional space to generate an equi-feasible ensemble of models. The reduced-dimensional space includes an approximated hypercube. Probable model samples are evaluated based on data misfits from among an equi-feasible model ensemble determined by forward numerical simulation. Final uncertainties are determined from the equivalent model ensemble and the final uncertainties are displayed in at least one map.
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申请公布号 |
US2013185033(A1) |
申请公布日期 |
2013.07.18 |
申请号 |
US201113634522 |
申请日期 |
2011.03.14 |
申请人 |
TOMPKINS MICHAEL J.;FERNANDEZ-MARTINEZ JUAN LUIS |
发明人 |
TOMPKINS MICHAEL J.;FERNANDEZ-MARTINEZ JUAN LUIS |
分类号 |
G06F17/50 |
主分类号 |
G06F17/50 |
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