Error handling strategies in multiphase inverse modeling
Parameter estimation by inverse modeling involves the repeated evaluation of a function of residuals. These residuals represent both errors in the model and errors in the data. In practical applications of inverse modeling of multiphase flow and transport, the error structure of the final residuals...
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| Veröffentlicht in: | Computers & geosciences 2011-06, Vol.37 (6), p.724-730 |
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| container_title | Computers & geosciences |
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| creator | Finsterle, Stefan Zhang, Yingqi |
| description | Parameter estimation by inverse modeling involves the repeated evaluation of a function of residuals. These residuals represent both errors in the model and errors in the data. In practical applications of inverse modeling of multiphase flow and transport, the error structure of the final residuals often significantly deviates from the statistical assumptions that underlie standard maximum likelihood estimation using the least-squares method. Large random or systematic errors are likely to lead to convergence problems, biased parameter estimates, misleading uncertainty measures, or poor predictive capabilities of the calibrated model. The multiphase inverse modeling code iTOUGH2 supports strategies that identify and mitigate the impact of systematic or non-normal error structures. We discuss these approaches and provide an overview of the error handling features implemented in iTOUGH2. |
| doi_str_mv | 10.1016/j.cageo.2010.11.009 |
| format | Article |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | 54 58 computers CONVERGENCE Earth sciences Earth, ocean, space Errors EVALUATION Exact sciences and technology Handling Hydrogeology Hydrology Hydrology. Hydrogeology Inverse Inverse modeling iTOUGH2 least squares Materials handling Mathematical models Multiphase MULTIPHASE FLOW Residual analysis Robust estimation SIMULATION Soils Strategy Surficial geology TRANSPORT uncertainty |
| title | Error handling strategies in multiphase inverse modeling |
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