@article{Symes2007,
author = {Symes},
title = {Reverse Time Migration with Optimal Checkpointing},
journal = {GEOPHYSICS},
volume = {72},
number = {5},
pages = {SM213-SM221},
year = {2007},
doi = {10.1190/1.2742686},
URL = {http://library.seg.org/doi/abs/10.1190/1.2742686},
eprint = {http://library.seg.org/doi/pdf/10.1190/1.2742686}
}

@article {Virieux,
		author = {J. Virieux and S. Operto},
		title = {An overview of full-waveform inversion in exploration geophysics},
		journal = {GEOPHYSICS},
		volume = {74},
		number = {5},
		pages = {WCC1-WCC26 },
		year = {2009},
		doi = {10.1190/1.3238367},
		URL = {http://library.seg.org/doi/abs/10.1190/1.3238367},
		eprint = {http://library.seg.org/doi/pdf/10.1190/1.3238367}
}

@article{Plessi,
		author = {Plessi},
		title = {A review of the adjoint-state method for computing the gradient of a functional with geophysical applications},
		journal = {Geophysical Journal International},
		volume = {167},
		number = {2},
		pages = {495-503},
		year = {2006},
		doi = {10.1111/j.1365-246X.2006.02978.x},
		URL = {http://gji.oxfordjournals.org/content/167/2/495.short},
		eprint = {http://gji.oxfordjournals.org/content/167/2/495.full.pdf}
}

@ARTICLE{Griewank2000ARA,
       author = "Andreas Griewank and Andrea Walther",
       title = "Algorithm 799: {R}evolve: {A}n Implementation of Checkpoint for the Reverse or Adjoint
         Mode of Computational Differentiation",
       note = "Also appeared as Technical University of Dresden, Technical Report IOKOMO-04-1997.",
       ad_theotech = "Checkpointing",
       journal = "{ACM} Transactions on Mathematical Software",
       volume = "26",
       number = "1",
       pages = "19--45",
       year = "2000",
       CODEN = "ACMSCU",
       ISSN = "0098-3500"
} 

@MISC{Clapp,
    author = {Clapp},
    title = {Reverse time migration with random boundaries},
    year = {2009},
	report ={Stanford Exploration Project},
	number={SEP138},
	url={http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.359.6496&rep=rep1&type=pdf#page=35}
}

@mastersthesis {Skajaa,
	year = {2010},
	school = {Courant Institute of Mathematical ScienceNew York University},
	type = {masters},
	author = {A. Skajaa},
	title = {Limited Memory BFGS for Nonsmooth Optimization},
	url = {http://cs.nyu.edu/overton/mstheses/skajaa/msthesis.pdf}
}

@conference {wason2013EAGEobs,
	title = {Ocean bottom seismic acquisition via jittered sampling},
	booktitle = {EAGE},
	year = {2013},
	month = {06},
	abstract = {We present a pragmatic marine acquisition scheme where multiple source vessels sail across an ocean-bottom array firing at airgunsjittered source locations and instances in time. Following the principles of compressive sensing, we can significantly impact the reconstruction quality of conventional seismic data (from jittered data) and demonstrate successful recovery by sparsity promotion. In contrast to random (under)sampling, acquisition via jittered (under)sampling helps in controlling the maximum gap size, which is a practical requirement of wavefield reconstruction with localized sparsifying transforms. Results are illustrated with simulations of time-jittered marine acquisition, which translates to jittered source locations for a given speed of the source vessel, for two source vessels.},
	keywords = {Acquisition, blended, deblending, EAGE, interpolation, marine},
	doi = {10.3997/2214-4609.20130379},
	url = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs.pdf},
	presentation = {https://www.slim.eos.ubc.ca/Publications/Public/Conferences/EAGE/2013/wason2013EAGEobs/wason2013EAGEobs_pres.pdf},
	author = {Haneet Wason and Felix J. Herrmann}
}

@article {hennenfent2008GEOPsdw,
	title = {Simply denoise: wavefield reconstruction via jittered undersampling},
	journal = {Geophysics},
	volume = {73},
	number = {3},
	year = {2008},
	month = {05},
	pages = {V19-V28},
	publisher = {SEG},
	abstract = {In this paper, we present a new discrete undersampling scheme designed to favor wavefield reconstruction by sparsity-promoting inversion with transform elements that are localized in the Fourier domain. Our work is motivated by empirical observations in the seismic community, corroborated by recent results from compressive sampling, which indicate favorable (wavefield) reconstructions from random as opposed to regular undersampling. As predicted by theory, 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 and the main purpose of this paper is to introduce a sampling scheme, coined jittered undersampling, that shares the benefits of random sampling, while offering control on the maximum gap size. Our contribution of jittered sub-Nyquist sampling proves to be key in the formulation of a versatile wavefield sparsity-promoting recovery scheme that follows the principles of compressive sampling. After studying the behavior of the jittered undersampling scheme in the Fourier domain, its performance is studied for curvelet recovery by sparsity-promoting inversion (CRSI). Our 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.},
	keywords = {Acquisition, Compressive Sensing, Geophysics, Optimization, Processing, sampling, SLIM},
	doi = {10.1190/1.2841038},
	url = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/hennenfent08GEOsdw.pdf},
	html_version = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2008/hennenfent08GEOsdw/paper_html/paper.html},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}

@article {vanLeeuwen2014SISC3Dfds,
	title = {{3D} frequency-domain seismic inversion with controlled sloppiness},
	journal = {SIAM Journal on Scientific Computing},
	volume = {36},
	number = {5},
	year = {2014},
	note = {(SISC)},
	month = {10},
	pages = {S192-S217},
	abstract = {Seismic waveform inversion aims at obtaining detailed estimates of subsurface medium parameters, such as the spatial distribution of soundspeed, from multiexperiment seismic data. A formulation of this inverse problem in the frequency domain leads to an optimization problem constrained by a Helmholtz equation with many right-hand sides. Application of this technique to industry-scale problems faces several challenges: First, we need to solve the Helmholtz equation for high wave numbers over large computational domains. Second, the data consist of many independent experiments, leading to a large number of PDE solves. This results in high computational complexity both in terms of memory and CPU time as well as input/output costs. Finally, the inverse problem is highly nonlinear and a lot of art goes into preprocessing and regularization. Ideally, an inversion needs to be run several times with different initial guesses and/or tuning parameters. In this paper, we discuss the requirements of the various components (PDE solver, optimization method, \dots) when applied to large-scale three-dimensional seismic waveform inversion and combine several existing approaches into a flexible inversion scheme for seismic waveform inversion. The scheme is based on the idea that in the early stages of the inversion we do not need all the data or very accurate PDE solves. We base our method on an existing preconditioned Krylov solver (CARP-CG) and use ideas from stochastic optimization to formulate a gradient-based (quasi-Newton) optimization algorithm that works with small subsets of the right-hand sides and uses inexact PDE solves for the gradient calculations. We propose novel heuristics to adaptively control both the accuracy and the number of right-hand sides. We illustrate the algorithms on synthetic benchmark models for which significant computational gains can be made without being sensitive to noise and without losing the accuracy of the inverted model.},
	keywords = {block-cg, Helmholtz equation, inexact gradient, Kaczmarz method, preconditioning, Seismic inversion},
	doi = {10.1137/130918629},
	url = {http://epubs.siam.org/doi/abs/10.1137/130918629},
	url2 = {https://www.slim.eos.ubc.ca/Publications/Public/Journals/SIAM_Journal_on_Scientific_Computing/2014/vanLeeuwen2014SISC3Dfds/vanLeeuwen2014SISC3Dfds.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}