Rajiv Kumar
Georgia Institute of Technology
53 Papers
168 Citations
Rajiv Kumar is an academic researcher from Georgia Institute of Technology. The author has contributed to research in topics: Interpolation & Matrix completion. The author has an hindex of 10, co-authored 53 publications. Previous affiliations of Rajiv Kumar include University of British Columbia.
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Papers
Efficient matrix completion for seismic data reconstruction
Rajiv Kumar,Curt Da Silva,Okan Akalin,Aleksandr Y. Aravkin,Hassan Mansour,Benjamin Recht,Felix J. Herrmann +6 more
TL;DR: In this article, a low-rank optimization technique was proposed to recover the missing trace of seismic data from the source and receiver coordinates, where the original signal is low rank and the subsampling scheme increases the singular values of the matrix.
99
Fast Methods for Denoising Matrix Completion Formulations, with Applications to Robust Seismic Data Interpolation
TL;DR: This paper considers matrix completion formulations designed to hit a target data-fitting error level provided by the user, and proposes an algorithm called LR-BPDN that is able to exploit factorized formulations to solve the corresponding optimization problem.
86
Source separation for simultaneous towed-streamer marine acquisition — A compressed sensing approach
TL;DR: This work has addressed the challenge of source separation for simultaneous towed-streamer acquisitions via two compressed-sensing-based approaches, i.e., sparsity promotion and rank minimization, and evaluated the performance of the sparsity-promotion- and rank-minimization-based techniques by simulating two simultaneous acquisitions.
51
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.
32
Seismic Data Interpolation and Denoising Using SVD-free Low-rank Matrix Factorization
Rajiv Kumar,Aleksandr Y. Aravkin,Hassan Mansour,Ben Recht,Felix J. Herrmann +4 more
- 10 Jun 2013
TL;DR: In this article, a robust rank-regularized approach is proposed for simultaneous seismic data interpolation and denoising using robust rank regularized formulations. But, the proposed approach is suitable for large scale problems, since it avoids SVD computations by using factorized formulations, and it can be used for both large-scale and small-scale problems.
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