@article{berkhout82smi,
author = {Berkhout, A. J. and Pao, Y. H.},
doi = {10.1115/1.3162563},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Berkhout, Pao/Berkhout, Pao - 1982 - Seismic Migration - Imaging of Acoustic Energy by Wave Field Extrapolation.pdf:pdf},
issn = {00218936},
journal = {Journal of Applied Mechanics},
month = sep,
number = {3},
pages = {682},
publisher = {ASME},
shorttitle = {J. Appl. Mech.},
title = {{Seismic Migration - Imaging of Acoustic Energy by Wave Field Extrapolation}},
url = {http://dx.doi.org/10.1115/1.3162563},
volume = {49},
year = {1982}
}
@article{Fleischer1999,
author = {Fleischer, Gunter and Gorenflo, R. and Hofmann, Bernd},
doi = {10.1002/(SICI)1521-4001(199903)79:3<149::AID-ZAMM149>3.0.CO;2-N},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Fleischer, Gorenflo, Hofmann/Fleischer, Gorenflo, Hofmann - 1999 - On the Autoconvolution Equation and Total Variation Constraints.pdf:pdf},
issn = {0044-2267},
journal = {ZAMM},
month = mar,
number = {3},
pages = {149--159},
title = {{On the Autoconvolution Equation and Total Variation Constraints}},
url = {http://doi.wiley.com/10.1002/\%28SICI\%291521-4001\%28199903\%2979\%3A3\%3C149\%3A\%3AAID-ZAMM149\%3E3.0.CO\%3B2-N},
volume = {79},
year = {1999}
}
@article{Fleischer1996,
author = {Fleischer, Gunter and Hofmann, Bernd},
doi = {10.1088/0266-5611/12/4/006},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Fleischer, Hofmann/Fleischer, Hofmann - 1996 - On inversion rates for the autoconvolution equation.pdf:pdf},
issn = {0266-5611},
journal = {Inverse Problems},
month = aug,
number = {4},
pages = {419--435},
title = {{On inversion rates for the autoconvolution equation}},
url = {http://stacks.iop.org/0266-5611/12/i=4/a=006?key=crossref.e89119a055c8a50c2153fd2c575d94b1},
volume = {12},
year = {1996}
}
@book{Fokkema1993,
abstract = {The seismic applications of the reciprocity theorem developed in this book are partly based on lecture notes and publications from Professor de Hoop. Every student Professor de Hoop has taught knows the egg-shaped figure (affectionately known as "de Hoop's egg") that plays such an important role in his theoretical description of acoustic, electromagnetic and elastodynamic wave phenomena.On the one hand this figure represents the domain for the application of a reciprocity theorem in the analysis of a wavefield and on the other hand it symbolizes the power of a consistent wavefield description of this theorem.The roots of the reciprocity theorem lie in Green's theorem for Laplace's equation and Helmholtz's extension to the wave equation. In 1894, J.W. Strutt, who later became Lord Rayleigh, introduced in his book The Theory of Sound this extension under the name of Helmholtz's theorem. Nowadays it is known as Rayleigh's reciprocity theorem.Progress in seismic data processing requires the knowledge of all the theoretical aspects of the acoustic wave theory. The reciprocity theorem was chosen as the central theme of this book as it constitutes the fundaments of the seismic wave theory. In essence, two states are distinguished in this theorem. These can be completely different, although sharing the same time-invariant domain of application, and they are related via an interaction quantity. The particular choice of the two states determines the acoustic application, in turn making it possible to formulate the seismic experiment in terms of a geological system response to a known source function.In linear system theory, it is well known that the response to a known input function can be written as an integral representation where the impulse response acts as a kernel and operates on the input function. Due to the temporal invariance of the system, this integral representation is of the convolution type. In seismics, the temporal behaviour of the system is dealt with in a similar fashion; however the spatial interaction needs a different approach. The reciprocity theorem handles this interaction by identifying one state with the spatial impulse function, also known as the Green's function, while the other state is connected with the actual source distribution. In general, the resulting integral representation is not a spatial convolution. Moreover, the systematic use of the reciprocity theorem leads to a hierarchical description of the seismic experiment in terms of increasing complexity. Also from an educational point of view this approach provides a hierarchy and the student learns to break down the seismic problem into constituent partial solutions.This book should contribute to the understanding that the reciprocity theorem is a powerful tool in the analysis of the seismic experiment.},
author = {Fokkema, J. T. and van den Berg, P. M.},
isbn = {0444890440},
pages = {350},
publisher = {Elsevier Science},
title = {{Seismic applications of acoustic reciprocity}},
url = {http://books.google.com/books?id=398SAQAAIAAJ\&pgis=1},
year = {1993}
}
@article{Frijlink2011,
author = {Frijlink, Martijn O. and van Borselen, Roald G. and S\"{o}llner, Walter},
doi = {10.1111/j.1365-2478.2010.00914.x},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Frijlink, van Borselen, S\"{o}llner/Frijlink, van Borselen, S\"{o}llner - 2011 - The free surface assumption for marine data-driven demultiple methods.pdf:pdf},
journal = {Geophysical Prospecting},
month = mar,
number = {2},
pages = {269--278},
title = {{The free surface assumption for marine data-driven demultiple methods}},
url = {http://doi.wiley.com/10.1111/j.1365-2478.2010.00914.x},
volume = {59},
year = {2011}
}
@article{Li2012a,
abstract = {ABSTRACTWave-equation-based seismic inversion can be formulated as a nonlinear least-squares problem. The demand for higher-resolution models in more geologically complex areas drives the need to develop techniques that exploit the special structure of full-waveform inversion to reduce the computational burden and to regularize the inverse problem. We meet these goals by using ideas from compressive sensing and stochastic optimization to design a novel Gauss-Newton method, where the updates are computed from random subsets of the data via curvelet-domain sparsity promotion. Two different subset sampling strategies are considered: randomized source encoding, and drawing sequential shots firing at random source locations from marine data with missing near and far offsets. In both cases, we obtain excellent inversion results compared to conventional methods at reduced computational costs.},
author = {Li, Xiang and Aravkin, Aleksandr Y. and van Leeuwen, Tristan and Herrmann, Felix J.},
doi = {10.1190/geo2011-0410.1},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Li et al/Li et al. - 2012 - Fast randomized full-waveform inversion with compressive sensing.pdf:pdf},
issn = {0016-8033},
journal = {Geophysics},
language = {en},
month = may,
number = {3},
pages = {A13--A17},
publisher = {Society of Exploration Geophysicists},
title = {{Fast randomized full-waveform inversion with compressive sensing}},
url = {http://library.seg.org/doi/abs/10.1190/geo2011-0410.1},
volume = {77},
year = {2012}
}
@conference{lin2014EAGEmas,
abstract = {We propose a method to substantially reduce the computational costs of the Robust Estimation of Primaries by Sparse Inversion algorithm, based on a multilevel inversion strategy that shifts early iterations of the method to successively coarser spatial sampling grids. This method requires no change in the core implementation of the original algorithm, and additionally only relies on trace decimation, low-pass filtering, and rudimentary interpolation techniques. We furthermore demonstrate with a synthetic seismic line significant computational speedups using this approach.},
author = {Lin, Tim T Y and Herrmann, Felix J},
booktitle = {76th EAGE Conference \& Exhibition},
keywords = {EAGE,EPSI,REPSI,multigrid,multilevel,multiples,multiscale},
title = {{Multilevel acceleration strategy for the robust estimation of primaries by sparse inversion}},
url = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2014/lin2014EAGEmas.pdf},
year = {2014}
}
@article{lin2013robustEPSI,
abstract = {A recently proposed method called estimation of primaries by sparse inversion (EPSI) avoids the need for adaptive subtraction of approximate multiple predictions by directly inverting for the multiple-free subsurface impulse response as a collection of band-limited spikes. Although it can be shown that the correct primary impulse response is obtained through the sparsest possible solution, the original EPSI algorithm was not designed to take advantage of this result, and instead it relies on a multitude of inversion parameters, such as the level of sparsity per gradient update. We proposed and tested a new algorithm, named robust EPSI, in which we make obtaining the sparsest solution an explicit goal. Our approach remains a gradient-based approach like the original algorithm, but it is derived from a new biconvex optimization framework based on an extended basis-pursuit denoising formulation. Furthermore, because it is based on a general framework, robust EPSI can recover the impulse response in transform domains, such as sparsifying curvelet-based representations, without changing the underlying algorithm. We discovered that the sparsity-minimizing objective of our formulation enabled it to operate successfully on a variety of synthetic and field marine data sets without excessive tweaking of inversion parameters. We also found that recovering the solution in alternate sparsity domains can significantly improve the quality of the directly estimated primaries, especially for weaker late-arrival events. In addition, we found that robust EPSI produces a more artifact-free impulse response compared to the original algorithm.},
author = {Lin, Tim T. Y. and Herrmann, Felix J.},
doi = {10.1190/geo2012-0097.1},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/Lin, Herrmann/Lin, Herrmann - 2013 - Robust estimation of primaries by sparse inversion via one-norm minimization.pdf:pdf},
journal = {Geophysics},
keywords = {algorithm,multiples,optimization,sparse,surface-related multiple elimination (SRME)},
language = {en},
month = may,
number = {3},
pages = {R133--R150},
publisher = {Society of Exploration Geophysicists},
title = {{Robust estimation of primaries by sparse inversion via one-norm minimization}},
url = {http://library.seg.org/doi/abs/10.1190/geo2012-0097.1},
volume = {78},
year = {2013}
}
@article{Tibshirani1996,
abstract = {We propose a new method for estimation in linear models. The `lasso' minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree-based models are briefly described.},
author = {Tibshirani, R},
institution = {Department of Statistics, University of Toronto},
issn = {00359246},
journal = {Journal of the Royal Statistical Society Series B Methodological},
number = {1},
pages = {267--288},
pmid = {2346178},
publisher = {JSTOR},
series = {B},
title = {{Regression shrinkage and selection via the lasso}},
url = {http://www.jstor.org/stable/2346178},
volume = {58},
year = {1996}
}
@article{groenestijn09eps,
abstract = {Accurate removal of surface-related multiples remains a challenge in many cases. To overcome typical inaccuracies in current multiple-removal techniques, we have developed a new primary-estimation method: estimation of primaries by sparse inversion (EPSI). EPSI is based on the same primary-multiple model as surface-related multiple elimination (SRME) and also requires no subsurface model. Unlike SRME, EPSI estimates the primaries as unknowns in a multidimensional inversion process rather than in a subtraction process. Furthermore, it does not depend on interpolated missing near-offset data because it can reconstruct missing data simultaneously. Sparseness plays a key role in the new primary-estimation procedure. The method was tested on 2D synthetic data.},
author = {van Groenestijn, G. J. A. and Verschuur, D. J.},
doi = {10.1190/1.3111115},
file = {:Volumes/Users/timlin/Documents/Mendeley\_Papers/van Groenestijn, Verschuur/van Groenestijn, Verschuur - 2009 - Estimating primaries by sparse inversion and application to near-offset data reconstruction.pdf:pdf},
journal = {Geophysics},
month = jan,
number = {3},
pages = {A23--A28},
title = {{Estimating primaries by sparse inversion and application to near-offset data reconstruction}},
volume = {74},
year = {2009}
}
@book{Verschuur2006,
author = {Verschuur, D. J.},
pages = {191},
publisher = {EAGE Publications},
title = {{Seismic multiple removal techniques: Past, present and future}},
year = {2006}
}
@article{Verschuur1992,
author = {Verschuur, D. J.},
doi = {10.1190/1.1443330},
journal = {Geophysics},
month = sep,
number = {9},
pages = {1166},
title = {{Adaptive surface-related multiple elimination}},
url = {http://library.seg.org/getabs/servlet/GetabsServlet?prog=normal\&id=GPYSA7000057000009001166000001\&idtype=cvips\&gifs=yes\&ref=no},
volume = {57},
year = {1992}
}