Advanced Methods of Joint Inversion and Fusion of Multiphysics Data

Author:   Michael S. Zhdanov
Publisher:   Springer Verlag, Singapore
Edition:   1st ed. 2023
ISBN:  

9789819967216


Pages:   369
Publication Date:   29 December 2023
Format:   Hardback
Availability:   Manufactured on demand   Availability explained
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Advanced Methods of Joint Inversion and Fusion of Multiphysics Data


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Overview

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.

Full Product Details

Author:   Michael S. Zhdanov
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
Edition:   1st ed. 2023
Weight:   0.817kg
ISBN:  

9789819967216


ISBN 10:   981996721
Pages:   369
Publication Date:   29 December 2023
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. INTRODUCTION TO INVERSION THEORY 2. ELEMENTS OF PROBABILITY THEORY 3. VECTOR SPACES OF MODELS AND DATA 4. PRINCIPLES OF REGULARIZATION THEORY 5. LINEAR INVERSE PROBLEMS 6. PROBABILISTIC METHODS OF INVERSE PROBLEM SOLUTION 7. GRADIENT-TYPE METHODS OF NON-LINEAR INVERSION 8. JOINT INVERSION BASED ON ANALYTICAL AND STATISTICAL RELATIONSHIPS BETWEEN DIFFERENT PHYSICAL PROPERTIES 9. JOINT INVERSION BASED ON STRUCTURAL SIMILARITIES 10. JOINT FOCUSING INVERSION OF MULTIPHYSICS DATA 11. JOINT MINIMUM ENTROPY INVERSION 12. GRAMIAN METHOD OF GENERALIZED JOINT INVERSION 13. PROBABILISTIC APPROACH TO GRAMIAN INVERSION 14. SIMULTANEOUS PROCESSING AND FUSION OF MULTIPHYSICS DATA AND IMAGES 15. MACHINE LEARNING IN THE CONTEXT OF INVERSION THEORY 16. MACHINE LEARNING INVERSION OF MULTIPHYSICS DATA 17. MODELING AND INVERSION OF POTENTIAL FIELD DATA 18. CASE HISTORIES OF JOINT INVERSION OF GRAVITY AND MAGNETIC DATA

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Author Information

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. 

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