Oscar Lopez
Sandia National Laboratories
11 Papers
24 Citations
Oscar Lopez is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Interpolation & Computer science. The author has an hindex of 3, co-authored 7 publications. Previous affiliations of Oscar Lopez include University of British Columbia.
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Papers
Off-the-Grid Low-Rank Matrix Recovery and Seismic Data Reconstruction
TL;DR: This paper proposes and analyzes a modified low-rank matrix recovery work-flow that admits unstructured observations and incorporates a regularization operator which accurately maps structured data to unstructuring data, into the nuclear-norm minimization problem, to compensate for data irregularity.
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Matrix Completion on Unstructured Grids - 2-D Seismic Data Regularization and Interpolation
Rajiv Kumar,Oscar Lopez,Ernie Esser,Felix J. Herrmann +3 more
- 01 Jun 2015
TL;DR: In this article, the effect of grid irregularity to conduct matrix completion on a regular grid for unstructured data is studied and an improvement of existing rank-minimization techniques to do regularization is proposed.
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Beating Level-Set Methods for 5-D Seismic Data Interpolation: A Primal-Dual Alternating Approach
TL;DR: In this article, a primal-dual splitting approach is proposed to solve residual constrained formulations for data interpolation, which is competitive with state-of-the-art level-set algorithms that interchange the role of objectives with constraints.
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Wavefield recovery with limited-subspace weighted matrix factorizations
TL;DR: A recursive recovery technique, which involves weighted matrix factorizations where recovered wavefields at the lower frequencies serve as prior information for the recovery of the higher frequencies, and results show that the limited-subspace weighted recovery method significantly improves the recovery quality.
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•Posted Content
Wavefield recovery with limited-subspace weighted matrix factorizations
TL;DR: In this article, the authors proposed a recursive matrix factorization approach to improve the performance of low-rank matrix factorizations at higher frequencies, where the size of the row and column subspaces to construct the weight matrices is constrained.
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