
- 718 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
About this book
Coding and Decoding Seismic Data: The Concept of Multishooting, Volume One, Second Edition, offers a thorough investigation of modern techniques for collecting, simulating, and processing multishooting data. Currently, the acquisition of seismic surveys is performed as a sequential operation in which shots are computed separately, one after the other. The cost of performing various shots simultaneously is almost identical to that of one shot; thus, the benefits of using the multishooting approach for computing seismic surveys are enormous.By using this approach, the longstanding problem of simulating a three-dimensional seismic survey can be reduced to a matter of weeks. Providing both theoretical and practical explanations of the multishooting approach, including case histories, this book is an essential resource for exploration geophysicists and practicing seismologists.- Investigates how to collect, stimulate, and process multishooting data- Addresses the improvements in seismic characterization and resolution that can be expected from multishooting data- Provides information for the oil and gas exploration and production business that will influence day-to-day surveying techniques- Covers robust decoding methods of undetermined mixtures, nonlinear decoding, the use of constraints in decoding processes, and nonlinear imaging of undecoded data- Includes access to a companion site with answers to questions posed in the book
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Information
Table of contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Preface (First Edition)
- Preface (Second Edition)
- Chapter 1: Introduction to Multishooting: Challenges and Rewards
- Chapter 2: Decoding of Linear Instantaneous Mixtures
- Chapter 3: Decoding of Linear Convolutive Mixtures
- Chapter 4: Decoding of Underdetermined Mixtures
- Chapter 5: Decoding of Nonlinear Mixtures
- Chapter 6: Imaging of Multishot Data Without Decoding
- Appendix A: Some Background on Sparsity Optimization
- Appendix B: ICA Decomposition
- Appendix C: Nonnegative Matrix Factorization
- Appendix D: Nonnegative Tensor Factorization
- Appendix E: A Review of 3D Finite-Difference Modeling
- BIBLIOGRAPHY
- Index