Uncertainty estimation for large-scale nonlinear inverse problems using geometric sampling and covariance-free model compression
US9619590B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 14, 2011 |
| Grant date | Apr 11, 2017 |
| Priority date | — |
| Expiry date | Nov 5, 2033 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F17/11
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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 orthogonal space. 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.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.