%% This BibTeX bibliography file was created using BibDesk. %% http://bibdesk.sourceforge.net/ %% Created for Ali Alfaraj at 2017-01-17 11:30:23 -0800 %% Saved with string encoding Unicode (UTF-8) @article{donoho2006compressed, title={Compressed sensing}, author={Donoho, David L}, journal={IEEE Transactions on information theory}, volume={52}, number={4}, pages={1289--1306}, year={2006}, publisher={IEEE}} @article{BeasleySimSr, author = {Craig J. Beasley}, title = {A new look at marine simultaneous sources}, journal = {The Leading Edge}, volume = {27}, number = {7}, pages = {914-917}, year = {2008}, doi = {10.1190/1.2954033}, URL = { http://dx.doi.org/10.1190/1.2954033}, eprint = { http://dx.doi.org/10.1190/1.2954033}} @conference{alfaraj2017EAGEswr, title = {Shear wave reconstruction from low cost randomized acquisition}, year = {2017}, abstract = {Shear waves travel in the subsurface at a lower speed compared with compressional waves. Therefore, much finer spatial sampling is required to properly record the shear waves. This leads to higher acquisition costs which are typically avoided by designing surveys geared towards only compressional waves imaging. We propose using randomly under-sampled ocean bottom acquisition for recording both compressional and shear waves. The recorded multicomponent data is then interpolated using an SVD-free low rank interpolation scheme that is feasible for large scale seismic data volumes to obtain finely sampled data. Following that, we perform elastic wavefield decomposition at the ocean bottom to recover accurate up- and dow-going S-waves. Synthetic data results indicate that using randomized under-sampled acquisition, we can recover accurate S-waves with an economical cost compared with conventional acquisition designs.}, keywords = {EAGE, interpolation, private, randomized acquisition, rank minimization, shear waves}, url = {https://www.slim.eos.ubc.ca/Publications/Private/Conferences/EAGE/2017/alfaraj2017EAGEswr/alfaraj2017EAGEswr.pdf}, booktitle = {79h EAGE Annual Conference Proceedings}, author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}} @incollection{wason2013time, title={Time-jittered ocean bottom seismic acquisition}, author={Wason, Haneet and Herrmann, Felix J}, booktitle={SEG Technical Program Expanded Abstracts 2013}, pages={1--6}, year={2013}, publisher={Society of Exploration Geophysicists}} @article{thorbecke2011finite, Author = {Thorbecke, Jan W and Draganov, Deyan}, Date-Added = {2017-01-16 00:12:09 +0000}, Date-Modified = {2017-01-16 00:12:22 +0000}, Journal = {Geophysics}, Number = {6}, Pages = {H1--H18}, Publisher = {Society of Exploration Geophysicists}, Title = {Finite-difference modeling experiments for seismic interferometry}, Volume = {76}, Year = {2011}} @book{aki2002quantitative, Author = {Aki, K. and Richards, P.G.}, Date-Added = {2017-01-12 10:23:47 +0000}, Date-Modified = {2017-01-12 10:23:47 +0000}, Isbn = {9780935702965}, Lccn = {2002071360}, Publisher = {University Science Books}, Series = {Geology (University Science Books).: Seismology}, Title = {Quantitative Seismology}, Url = {https://books.google.ca/books?id=sRhawFG5\_EcC}, Year = {2002}, Bdsk-Url-1 = {https://books.google.ca/books?id=sRhawFG5%5C_EcC}} @article{RechtRe:2011, Author = {Recht, Benjamin and R{\'e}, Christopher}, Journal = {Mathematical Programming Computation}, Number = {2}, Pages = {201--226}, Publisher = {Springer}, Title = {Parallel stochastic gradient algorithms for large-scale matrix completion}, Volume = {5}, Year = {2013}} @article{candes2008introduction, Author = {Cand{\`e}s, Emmanuel J and Wakin, Michael B}, Journal = {IEEE signal processing magazine}, Number = {2}, Pages = {21--30}, Publisher = {IEEE}, Title = {An introduction to compressive sampling}, Volume = {25}, Year = {2008}} @inproceedings{lee2010, Author = {Lee, Jason D and Recht, Ben and Srebro, Nathan and Tropp, Joel and Salakhutdinov, Ruslan R}, Booktitle = {Advances in Neural Information Processing Systems}, Pages = {1297--1305}, Title = {Practical large-scale optimization for max-norm regularization}, Year = {2010}} @article{SchalkwijkAd, Author = {K. M. Schalkwijk and C. P. A. Wapenaar and D. J. Verschuur}, Date-Added = {2017-01-11 22:53:47 +0000}, Date-Modified = {2017-01-11 22:54:03 +0000}, Doi = {10.1190/1.1581081}, Eprint = {http://dx.doi.org/10.1190/1.1581081}, Journal = {GEOPHYSICS}, Number = {3}, Pages = {1091-1102}, Title = {Adaptive decomposition of multicomponent ocean‐bottom seismic data into downgoing and upgoing P‐ and S‐waves}, Url = {http://dx.doi.org/10.1190/1.1581081}, Volume = {68}, Year = {2003}, Bdsk-Url-1 = {http://dx.doi.org/10.1190/1.1581081}} @conference{Alfaraj15, Author = {Ali M. Alfaraj and D.J. Verschuur and Kees Wapenaar}, Booktitle = {SEG Technical Program Expanded Abstracts 2015}, Date-Added = {2017-01-11 22:52:28 +0000}, Date-Modified = {2017-01-17 19:29:58 +0000}, Doi = {10.1190/segam2015-5918497.1}, Eprint = {http://library.seg.org/doi/pdf/10.1190/segam2015-5918497.1}, Pages = {2108-2112}, Title = {Near-Ocean Bottom Wavefield Tomography for Elastic Wavefield Decomposition}, Url = {http://library.seg.org/doi/abs/10.1190/segam2015-5918497.1}, Year = {2015}, Bdsk-Url-1 = {http://dx.doi.org/10.1190/segam2015-5918497.1}, Bdsk-Url-2 = {http://library.seg.org/doi/abs/10.1190/segam2015-5918497.1}} @book{wapenaar2014elastic, Author = {Wapenaar, C.P.A. and Berkhout, A.J.}, Date-Added = {2017-01-11 21:22:44 +0000}, Date-Modified = {2017-01-11 21:22:44 +0000}, Isbn = {9781483291000}, Publisher = {Elsevier Science}, Series = {Advances in Exploration Geophysics}, Title = {Elastic Wave Field Extrapolation: Redatuming of Single- and Multi-Component Seismic Data}, Url = {https://books.google.ca/books?id=as7-BAAAQBAJ}, Year = {2014}, Bdsk-Url-1 = {https://books.google.ca/books?id=as7-BAAAQBAJ}} @conference{hennenfent2006SEGaos, Abstract = {We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the 1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples. {\copyright}2006 Society of Exploration Geophysicists}, Author = {Gilles Hennenfent and Felix J. Herrmann}, Booktitle = {SEG Technical Program Expanded Abstracts}, Date-Added = {2017-01-11 21:14:42 +0000}, Date-Modified = {2017-01-11 21:14:42 +0000}, Doi = {10.1190/1.2370105}, Keywords = {amplitude, continuity, curvelets, fast transform, interpolation, iterative thresholding, Presentation, regularization minimization, SEG, seismic data, SLIM}, Organization = {SEG}, Pages = {2797-2801}, Presentation = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos_pres.pdf}, Publisher = {SEG}, Title = {Application of stable signal recovery to seismic data interpolation}, Url = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos.pdf}, Url2 = {http://dx.doi.org/10.1190/1.2370105}, Volume = {25}, Year = {2006}, Bdsk-Url-1 = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/SEG/2006/hennenfent06SEGaos/hennenfent06SEGaos.pdf}, Bdsk-Url-2 = {http://dx.doi.org/10.1190/1.2370105}} @article{Herrmann11TRfcd, Abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are mined for information during processing. This approach has been extremely successful, but current efforts toward higher resolution images in increasingly complicated regions of Earth continue to reveal fundamental shortcomings in our typical workflows. The "curse" of dimensionality is the main roadblock and is exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continues to increase.}, Address = {University of British Columbia, Vancouver}, Author = {Felix J. Herrmann and Michael P. Friedlander and Ozgur Yilmaz}, Date-Added = {2017-01-11 21:12:19 +0000}, Date-Modified = {2017-01-11 21:12:19 +0000}, Doi = {10.1109/MSP.2012.2185859}, Issn = {1053-5888}, Journal = {Signal Processing Magazine, IEEE}, Keywords = {dimensionality curse, Earth, geographic information systems, higher-resolution images, massive data volumes, Nyquist sampling criterion, seismic exploration techniques, seismology, strains current acquisition system, strains current processing system}, Month = {5}, Number = {3}, Pages = {88-100}, Title = {Fighting the curse of dimensionality: compressive sensing in exploration seismology}, Url = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/IEEESignalProcessingMagazine/2012/Herrmann11TRfcd/Herrmann11TRfcd.pdf}, Volume = {29}, Year = {2012}, Bdsk-Url-1 = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/IEEESignalProcessingMagazine/2012/Herrmann11TRfcd/Herrmann11TRfcd.pdf}, Bdsk-Url-2 = {http://dx.doi.org/10.1109/MSP.2012.2185859}} @article{stewart2003converted, Author = {Stewart, Robert R and Gaiser, James E and Brown, R James and Lawton, Don C}, Date-Added = {2017-01-11 20:56:10 +0000}, Date-Modified = {2017-01-11 20:56:21 +0000}, Journal = {Geophysics}, Number = {1}, Pages = {40--57}, Publisher = {Society of Exploration Geophysicists}, Title = {Converted-wave seismic exploration: Applications}, Volume = {68}, Year = {2003}} @article{wapenaar1990decomposition, Author = {Wapenaar, CPA and Herrmann, P and Verschuur, DJ and Berkhout, AJ}, Date-Added = {2017-01-11 20:53:14 +0000}, Date-Modified = {2017-01-11 20:53:22 +0000}, Journal = {Geophysical Prospecting}, Number = {6}, Pages = {633--661}, Title = {Decomposition of multicomponent seismic data into primary P-and S-wave responses}, Volume = {38}, Year = {1990}} @article{kumar2014GEOPemc, Abstract = {Despite recent developments in improved acquisition, seismic data often remain undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We have interpreted the missing-trace interpolation problem in the context of matrix completion (MC), and developed three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We use an optimization program that restores this low-rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we found experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by using the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, to work with computationally feasible algorithms for large-scale data, we use a factorization-based approach to MC, which significantly speeds up the computations compared with repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also find that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carried out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic 2D and 3D seismic data sets show that our method compares favorably in terms of computational speed and recovery quality with existing curvelet- and tensor-based techniques.}, Author = {Rajiv Kumar and Curt Da Silva and Okan Akalin and Aleksandr Y. Aravkin and Hassan Mansour and Ben Recht and Felix J. Herrmann}, Date-Added = {2017-01-11 20:48:09 +0000}, Date-Modified = {2017-01-11 20:48:09 +0000}, Doi = {10.1190/geo2014-0369.1}, Journal = {Geophysics}, Keywords = {2D, 3D, algorithm, interpolation, low-rank, signal processing}, Month = {09}, Note = {(Geophysics)}, Number = {05}, Pages = {V97-V114}, Publisher = {UBC}, Title = {Efficient matrix completion for seismic data reconstruction}, Url = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2015/kumar2014GEOPemc/kumar2014GEOPemc.pdf}, Url2 = {http://library.seg.org/doi/abs/10.1190/geo2014-0369.1}, Volume = {80}, Year = {2015}, Bdsk-Url-1 = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2015/kumar2014GEOPemc/kumar2014GEOPemc.pdf}, Bdsk-Url-2 = {http://dx.doi.org/10.1190/geo2014-0369.1}} @article{ZwartjesV21, Abstract = {There are numerous methods for interpolating uniformly sampled, aliased seismic data, but few can handle the combination of nonuniform sampling and aliasing. We combine the principles of Fourier reconstruction of nonaliased, nonuniformly sampled data with the ideas of frequency-wavenumber (f-k) interpolation of aliased, uniformly sampled data in a new two-step algorithm. In the first step, we estimate the Fourier coefficients at the lower nonaliased temporal frequencies from the nonuniformly sampled data. The coefficients are then used in the second step as an a priori model to distinguish between aliased and nonaliased energy at the higher, aliased temporal frequencies. By using a nonquadratic model penalty in the inversion, both the artifacts in the Fourier domain from nonuniform sampling and the aliased energy are suppressed. The underlying assumption is that events are planar; therefore, the algorithm is applied to seismic data in overlapping spatiotemporal windows.}, Author = {Zwartjes, P. M. and Sacchi, M. D.}, Date-Added = {2017-01-11 20:43:10 +0000}, Date-Modified = {2017-01-11 20:43:10 +0000}, Doi = {10.1190/1.2399442}, Eprint = {http://geophysics.geoscienceworld.org/content/72/1/V21.full.pdf}, Issn = {0016-8033}, Journal = {Geophysics}, Number = {1}, Pages = {V21--V32}, Publisher = {Society of Exploration Geophysicists}, Title = {Fourier reconstruction of nonuniformly sampled, aliased seismic data}, Url = {http://geophysics.geoscienceworld.org/content/72/1/V21}, Volume = {72}, Year = {2007}, Bdsk-Url-1 = {http://geophysics.geoscienceworld.org/content/72/1/V21}, Bdsk-Url-2 = {http://dx.doi.org/10.1190/1.2399442}} @article{tradRadInt, Author = {Daniel O. Trad and Tadeusz J. Ulrych and Mauricio D. Sacchi}, Date-Added = {2017-01-11 20:36:32 +0000}, Date-Modified = {2017-01-11 21:08:57 +0000}, Doi = {10.1190/1.1468626}, Eprint = {http://dx.doi.org/10.1190/1.1468626}, Journal = {GEOPHYSICS}, Number = {2}, Pages = {644-656}, Title = {Accurate interpolation with high‐resolution time‐variant Radon transforms}, Url = {http://dx.doi.org/10.1190/1.1468626}, Volume = {67}, Year = {2002}, Bdsk-Url-1 = {http://dx.doi.org/10.1190/1.1468626}} @article{StatSach13, Author = {Aaron Stanton and Mauricio D. Sacchi}, Date-Added = {2017-01-11 20:36:29 +0000}, Date-Modified = {2017-01-11 21:26:34 +0000}, Doi = {10.1190/geo2012-0448.1}, Eprint = {http://dx.doi.org/10.1190/geo2012-0448.1}, Journal = {GEOPHYSICS}, Number = {4}, Pages = {V131-V145}, Title = {Vector reconstruction of multicomponent seismic data}, Url = {http://dx.doi.org/10.1190/geo2012-0448.1}, Volume = {78}, Year = {2013}, Bdsk-Url-1 = {http://dx.doi.org/10.1190/geo2012-0448.1}} @article{HennenfentV19, Abstract = {We present a new, discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements localized in the Fourier domain. The work is motivated by empirical observations in the seismic community, corroborated by results from compressive sampling, that indicate favorable (wavefield) reconstructions from random rather than regular undersampling. Indeed, random undersampling renders coherent aliases into harmless incoherent random noise, effectively turning the interpolation problem into a much simpler denoising problem. A practical requirement of wavefield reconstruction with localized sparsifying transforms is the control on the maximum gap size. Unfortunately, random undersampling does not provide such a control. Thus, we introduce a sampling scheme, termed jittered undersampling, that shares the benefits of random sampling and controls the maximum gap size. The contribution of jittered sub-Nyquist sampling is key in formu-lating a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After the behavior of the jittered-undersampling scheme in the Fourier domain is analyzed, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). The findings on synthetic and real seismic data indicate an improvement of several decibels over recovery from regularly undersampled data for the same amount of data collected.}, Author = {Hennenfent, Gilles and Herrmann, Felix J.}, Date-Added = {2017-01-11 20:36:13 +0000}, Date-Modified = {2017-01-11 20:36:13 +0000}, Doi = {10.1190/1.2841038}, Eprint = {http://geophysics.geoscienceworld.org/content/73/3/V19.full.pdf}, Issn = {0016-8033}, Journal = {Geophysics}, Number = {3}, Pages = {V19--V28}, Publisher = {Society of Exploration Geophysicists}, Title = {Simply denoise: Wavefield reconstruction via jittered undersampling}, Url = {http://geophysics.geoscienceworld.org/content/73/3/V19}, Volume = {73}, Year = {2008}, Bdsk-Url-1 = {http://geophysics.geoscienceworld.org/content/73/3/V19}, Bdsk-Url-2 = {http://dx.doi.org/10.1190/1.2841038}} @article{KabirVersInt, Author = {Kabir, M.M. Nurul and Verschuur, D.J.}, Date-Added = {2017-01-11 20:36:08 +0000}, Date-Modified = {2017-01-11 21:13:51 +0000}, Doi = {10.1111/j.1365-2478.1995.tb00257.x}, Issn = {1365-2478}, Journal = {Geophysical Prospecting}, Number = {3}, Pages = {347--368}, Publisher = {Blackwell Publishing Ltd}, Title = {Restoration of missing offsets by parabolic Radon transform1}, Url = {http://dx.doi.org/10.1111/j.1365-2478.1995.tb00257.x}, Volume = {43}, Year = {1995}, Bdsk-File-1 = {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}, Bdsk-Url-1 = {http://dx.doi.org/10.1111/j.1365-2478.1995.tb00257.x}} @article{RechtFazelParrilo2010, Author = {B. Recht and M Fazel and P.A. Parrilo}, Journal = {SIAM Review}, Number = {3}, Pages = {471-501}, Title = {Guaranteed Minimum Rank Solutions to Linear Matrix Equations via Nuclear Norm Minimization.}, Volume = {52}, Year = {2010}} @article{AravkinBurkeFriedlander:2012, Author = {A.Y. Aravkin and J.V. Burke and M.P. Friedlander}, Journal = {submitted to SIAM J. Opt., arXiv:1211.3724}, Title = {Variational Properties of Value Functions}, Year = {2012}} @article{BergFriedlander:2008, Author = {E. {van den} Berg and M. P. Friedlander}, Journal = {SIAM Journal on Scientific Computing}, Keywords = {basis pursuit, convex program, duality, root-finding, Newton's method, projected gradient, one-norm regularization, sparse solutions}, Number = {2}, Pages = {890-912}, Publisher = {SIAM}, Title = {Probing the Pareto frontier for basis pursuit solutions}, Volume = {31}, Year = {2008}} @inproceedings{Rennie2005, Acmid = {1102441}, Address = {New York, NY, USA}, Author = {Rennie, Jasson D. M. and Srebro, Nathan}, Booktitle = {Proceedings of the 22nd international conference on Machine learning}, Isbn = {1-59593-180-5}, Location = {Bonn, Germany}, Numpages = {7}, Pages = {713--719}, Publisher = {ACM}, Series = {ICML '05}, Title = {Fast maximum margin matrix factorization for collaborative prediction}, Year = {2005}} @phdthesis{Srebro14, Author = {Nathan Srebro}, Booktitle = {PhD Thesis, MASSACHUSETTS INSTITUTE OF TECHNOLOGY}, School = {Massachusetts Institute of Technology}, Title = {Learning with matrix factorizations, PhD Thesis}, Url = {http://hdl.handle.net/1721.1/28743}, Year = {2004}, Bdsk-Url-1 = {http://hdl.handle.net/1721.1/28743}}