| 期刊全稱 | Advanced Methods of Joint Inversion and Fusion of Multiphysics Data | | 影響因子2023 | Michael S. Zhdanov | | 視頻video | http://file.papertrans.cn/146/145950/145950.mp4 | | 發(fā)行地址 | Presents the most recent advances, trending topics, and novel methods of integrated interpretation of multiphysics data.Is the first book to consider the complex challenges of joint inversion of diffe | | 學科分類 | Advances in Geological Science | | 圖書封面 |  | | 影響因子 | Different physical or geophysical methods provide information about distinctive physical properties of the objects, e.g., rock formations and mineralization. In many cases, this information is mutually complementary, which makes it natural for consideration in a joint inversion of the multiphysics data. Inversion of the observed data for a particular experiment is subject to considerable uncertainty and ambiguity. One productive approach to reducing uncertainty is to invert several types of data jointly. Nonuniqueness can also be reduced by incorporating additional information derived from available a priori knowledge about the target to reduce the search space for the solution. This additional information can be incorporated in the form of a joint inversion of multiphysics data..Generally established joint inversion methods, however, are inadequate for incorporating typical physical or geological complexity. For example, analytic, empirical, or statistical correlations between different physical properties may exist for only part of the model, and their specific form may be unknown. Features or structures that are present in the data of one physical method may not be present in th | | Pindex | Book 2023 |
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