UID:
almahu_9950000936202882
Format:
XIV, 239 p. 82 illus., 81 illus. in color.
,
online resource.
Edition:
1st ed. 2025.
ISBN:
9783031789007
Series Statement:
Earth Systems Data and Models, 6
Content:
This book aims to provide a comprehensive understanding of tensor computation and its applications in seismic data analysis, exclusively catering to seasoned researchers, graduate students, and industrial engineers alike. Tensor emerges as a natural representation of multi-dimensional modern seismic data, and tensor computation can help prevent possible harm to the multi-dimensional geological structure of the subsurface that occurred in classical seismic data analysis. It delivers a wealth of theoretical, computational, technical, and experimental details, presenting an engineer's perspective on tensor computation and an extensive investigation of tensor-based seismic data analysis techniques. Embark on a transformative exploration of seismic data processing-unlock the potential of tensor computation and reshape your approach to high-dimensional geological structures. The discussion begins with foundational chapters, providing a solid background in both seismic data processing and tensor computation. The heart of the book lies in its seven chapters on tensor-based seismic data analysis methods. From structured low-tubal-rank tensor completion to cutting-edge techniques like tensor deep learning and tensor convolutional neural networks, each method is meticulously detailed. The superiority of tensor-based data analysis methods over traditional matrix-based data analysis approaches is substantiated through synthetic and real field examples, showcasing their prowess in handling high-dimensional modern seismic data. Notable chapters delve into seismic noise suppression, seismic data interpolation, and seismic data super-resolution using advanced tensor models. The final chapter provides a cohesive summary of the conclusion and future research directions, ensuring readers facilitate a thorough understanding of tensor computation applications in seismic data processing. The appendix includes a hatful of information on existing tensor computation software, enhancing the book's practical utility.
Note:
Introduction -- The Foundations of Tensor Computation -- Tensor Completion for Seismic Data Reconstruction -- Tensor Low Rank Approximation for Seismic Footprint Suppression -- Tensor Deep Learning for Seismic Data Interpolation -- Transform Based Tensor Deep Learning for Seismic Random Noise Attenuation -- Order 𝒑 Tensor Deep Learning for Seismic Data Denoising -- Robust Tensor Deep Learning for Seismic Erratic Noise Attenuation -- Tensor Dictionary Learning for Seismic Data Super Resolution -- Conclusion and Future Research Directions.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031788994
Additional Edition:
Printed edition: ISBN 9783031789014
Additional Edition:
Printed edition: ISBN 9783031789021
Language:
English
DOI:
10.1007/978-3-031-78900-7
URL:
https://doi.org/10.1007/978-3-031-78900-7
Bookmarklink
