Tensor Computation for Seismic Data Processing : Linking Theory and Practice / by Feng Qian, Shengli Pan, Gulan Zhang

Von:

Qian, Feng [VerfasserIn]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Earth Systems Data and Models ; 6Verlag: Cham : Springer Nature Switzerland, 2025Verlag: Cham : Imprint: Springer, 2025Auflage: 1st ed. 2025Beschreibung: 1 Online-Ressource(XIV, 239 p. 82 illus., 81 illus. in color.)ISBN:

9783031789007

Schlagwörter:

550 23

DOI: DOI: 10.1007/978-3-031-78900-7Online-Ressourcen:

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Zusammenfassung: 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.Zusammenfassung: 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.PPN: PPN: 1924005638Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-EES | ZDB-2-SXEE

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