Advanced Methods of Joint Inversion and Fusion of Multiphysics Data - Rilegato

Zhdanov, Michael

Rilegato

ISBN 10: 981996721X ISBN 13: 9789819967216

Casa editrice: Springer Nature, 2023

Riassunto

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 the data generated by another physical method or may not be equally resolvable.

This book presents and illustrates several advanced, new approaches to joint inversion and data fusion, which do not require a priori knowledge of specific empirical or statistical relationships between the different model parameters or their attributes. These approaches include the following novel methods, among others: 1) the Gramian method, which enforces the correlation between different parameters; 2) joint total variation functional or joint focusing stabilizers, e.g., minimum support and minimum gradient support constraints; 3) data fusion employing a joint minimum entropy stabilizer, which yields the simplest multiphysics solution that fits the multi-modal data. In addition, the book describes the principles of using artificial intelligence (AI) in solving multiphysics inverse problems. The book also presents in detail both the mathematical principles of these advanced approaches to joint inversion of multiphysics data and successful case histories of regional-scale and deposit-scale geophysical studies to illustrate their indicated advantages.

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Informazioni sull?autore

Dr. Michael Zhdanov is a distinguished professor in the Department of Geology and Geophysics at the University of Utah in Salt Lake City, USA, and the director of the Consortium for Electromagnetic Modeling and Inversion (CEMI). He is also the founder and CEO of TechnoImaging LLC. Dr. Zhdanov is a leading expert in the field of theoretical and applied geophysics and is the author of more than 300 papers, including more than 15 monographs published in English, Russian, and Chinese, and holds in excess of two dozen patents. Professor Zhdanov is a full member of the Russian Academy of Natural Sciences and a fellow of the Electromagnetics Academy, USA. He received one of the highest awards of the International Society of Exploration Geophysicists, an honorary membership award, in recognition of his distinguished contributions to exploration geophysics and to the advancement of the profession. 

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 the data generated by another physical method or may not be equally resolvable.

This book presents and illustrates several advanced, new approaches to joint inversion and data fusion, which do not require a priori knowledge of specific empirical or statistical relationships between the different model parameters or their attributes. These approaches include the following novel methods, among others: 1) the Gramian method, which enforces the correlation between different parameters; 2) joint total variation functional or joint focusing stabilizers, e.g., minimum support and minimum gradient support constraints; 3) data fusion employing a joint minimum entropy stabilizer, which yields the simplest multiphysics solution that fits the multi-modal data. In addition, the book describes the principles of using artificial intelligence (AI) in solving multiphysics inverse problems. The book also presents in detail both the mathematical principles of these advanced approaches to joint inversion of multiphysics data and successful case histories of regional-scale and deposit-scale geophysical studies to illustrate their indicated advantages.

Dettagli dell'edizione

• Editore: Springer Nature
• Data di pubblicazione: 2023
• Lingua: Inglese
• ISBN 10: 981996721X
• ISBN 13: 9789819967216
• Rilegatura: Copertina rigida
• Numero edizione: 1
• Numero di pagine: 388
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania