Improving seismic data completion via low-rank tensor optimization
Author:
Affiliation:
1. University of Texas at Dallas
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2020-3423985.1
Reference20 articles.
1. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
2. Carozzi, F., and M. D. Sacchi, 2017, 5D seismic reconstruction via PMF with randomized QR decomposition: 87th Annual International Meeting, SEG, Expanded Abstracts, 4251–4256, doi: 10.1190/segam2017-17782228.1.
3. Cheng, J., and M. D. Sacchi, 2015, A fast rank-reduction algorithm for 3D deblending via randomized QR decomposition: 85th Annual International Meeting, SEG, Expanded Abstracts, 3830–3835, doi: 10.1190/segam2015-5850767.1.
4. 5D seismic data completion and denoising using a novel class of tensor decompositions
5. Parallel matrix factorization algorithm and its application to 5D seismic reconstruction and denoising
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1. A low-rank tensor reconstruction and denoising method for enhancing CNN performance;2024 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI);2024-03-17
2. Tensor-based reconstruction applied to regularized time-lapse data;Geophysical Journal International;2022-06-10
3. An improved seismic data completion algorithm using low-rank tensor optimization: Cost reduction and optimal data orientation;GEOPHYSICS;2021-04-21
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