Low-rank representation of subsurface extended image volumes with power iterations-Reference-Cited by-同舟云学术

Low-rank representation of subsurface extended image volumes with power iterations

Published:2019-08-10 Issue: Volume: Page:
ISSN:
Container-title:SEG Technical Program Expanded Abstracts 2019
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Author:

Yang Mengmeng¹,Graff Marie²,Kumar Rajiv³,Herrmann Felix J.⁴

Affiliation:

1. Seismic Laboratory for Imaging and Modeling (SLIM), Georgia Institute of Technology, USA

2. Department of Mathematics, University of Auckland, New Zealand

3. DownUnder GeoSolution, Perth, Australia

4. Seismic Laboratory for Imaging and Modeling (SLIM), Georgia Institute of Technology, USA, and School of Computational Science and Engineering, Georgia Institute of Technology, USA

Publisher

Society of Exploration Geophysicists

Link

https://library.seg.org/doi/pdf/10.1190/segam2019-3215961.1

Reference12 articles.

1. Applications of low-rank compressed seismic data to full-waveform inversion and extended image volumes

2. Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions

3. Kumar, R., M., Graff-Kray, T., van Leeuwen, and F. J., Herrmann, 2018, Low-rank representation of extended image volumes –– Applications to imaging and velocity continuation: 88th Annual International Meeting, SEG, Expanded Abstracts, 4418–4422, doi: 10.1190/segam2018-2998404.1.

4. Lange, M., N., Kukreja, M., Louboutin, F., Luporini, F., Vieira, V., Pandolfo, P., Velesko, P., Kazakas, and G., Gorman, 2016, Devito: Towards a generic finite difference DSL using symbolic python: 6th IEEE Workshop on Python for High-Performance and Scientific Computing, 67–75.

5. Musco, C., and C., Musco, 2015, Randomized block Krylov methods for stronger and faster approximate singular value decomposition: Advances in Neural Information Processing Systems, 1396–1404.

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