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Tensor computation for seismic data ...

Qian, Feng.

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Tensor computation for seismic data processing = linking theory and practice /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Tensor computation for seismic data processing/ by Feng Qian, Shengli Pan, Gulan Zhang.
Reminder of title:	linking theory and practice /
Author:	Qian, Feng.
other author:	Pan, Shengli.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 239 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Calculus of tensors. -
Online resource:	https://doi.org/10.1007/978-3-031-78900-7
ISBN:	9783031789007

Tensor computation for seismic data processing = linking theory and practice /
Qian, Feng.

Tensor computation for seismic data processinglinking theory and practice /[electronic resource] :by Feng Qian, Shengli Pan, Gulan Zhang. - Cham :Springer Nature Switzerland :2025. - xiv, 239 p. :ill. (some col.), digital ;24 cm. - Earth systems data and models,v. 62364-5849 ;. - Earth systems data and models ;v. 6..

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 �� 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.

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.

ISBN: 9783031789007

Standard No.: 10.1007/978-3-031-78900-7doiSubjects--Topical Terms:

533863
Calculus of tensors.

LC Class. No.: QE539.2.S4

Dewey Class. No.: 551.22015118

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505 0 $a 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.
520 $a 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.
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