@techreport {louboutin2020SCsta,
	title = {Scaling through abstractions {\textendash} high-performance vectorial wave simulations for seismic inversion with Devito},
	number = {TR-CSE-2020-3},
	year = {2020},
	month = {4},
	institution = {Georgia Institute of Technology},
	abstract = {[Devito] is an open-source Python project based on domain-specific language and compiler technology. Driven by the requirements of rapid HPC applications development in exploration seismology, the language and compiler have evolved significantly since inception. Sophisticated boundary conditions, tensor contractions, sparse operations and features such as staggered grids and sub-domains are all supported; operators of essentially arbitrary complexity can be generated. To accommodate this flexibility whilst ensuring performance, data dependency analysis is utilized to schedule loops and detect computational-properties such as parallelism. In this article, the generation and simulation of MPI-parallel propagators (along with their adjoints) for the pseudo-acoustic wave-equation in tilted transverse isotropic media and the elastic wave-equation are presented. Simulations are carried out on industry scale synthetic models in a HPC Cloud system and reach a performance of 28TFLOP/s, hence demonstrating Devito{\textquoteright}s suitability for production-grade seismic inversion problems.},
	keywords = {devito, elastic, finite-difference, HPC, large-scale, RTM, TTI},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2020/louboutin2020SCsta/louboutin2020SCsta.html},
	author = {Mathias Louboutin and Fabio Luporini and Philipp A. Witte and Rhodri Nelson and George Bisbas and Jan Thorbecke and Felix J. Herrmann and Gerard Gorman}
}
@techreport {kumar2015EAGElse,
	title = {Least-squares extended imaging with surface-related multiples},
	number = {TR-EOAS-2015-1},
	year = {2015},
	month = {01},
	institution = {UBC},
	abstract = {Common image gathers are used in building velocity models, inverting for anisotropy parameters, and analyzing reservoir attributes. Typically, only primary reflections are used to form image gathers as multiples can cause artifacts that interfere with the events of interest. However, it has been shown that multiples can actually provide extra illumination of the subsurface, especially for delineating the near-surface features. In this paper, we aim to form common image gathers directly from the data with surface related multiples by applying concepts that have been used to successfully deal with surface-related multiples in imaging. We achieve this by effectively inverting an extended migration operator. This results in extended images with better near-surface illumination that are free of artifacts that can hamper velocity analysis. In addition, being able to generate extended images directly from the total data avoids the need for (time-consuming) pre-processing. Synthetic examples on a layered model show that the proposed formulation is promising.},
	keywords = {image gathers, surface-related multiples},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/kumar2015EAGElse/kumar2015EAGElse.html},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@techreport {tschannen2014tdl,
	title = {Time domain least squares migration and dimensionality reduction},
	number = {TR-EOAS-2014-9},
	year = {2014},
	month = {06},
	institution = {UBC},
	abstract = {Least-squares seismic migration (LSM) is a wave equation based linearized inversion problem relying on the minimization of a least-squares misfit function, with respect to the medium perturbation, between recorded and modeled wavefields. Today{\textquoteright}s challenges in Hydrocarbon ex- ploration are to build high resolution images of more and more complicated geological reservoirs, which requires to handle very large systems of equations. The extreme size of the problem com- bined with the fact that it is ill-conditioned make LSM not yet feasible for industrial purposes. To overcome this "curse of dimensionality", dimension reduction and divide-and-conquer tech- niques that aim to decrease the computation time and the required memory, while conserving the image quality, have recently been developed. By borrowing ideas from stochastic optimiza- tion and compressive sensing, the imaging problem is reformulated as an L1-regularized, sparsity promoted LSM. The idea is to take advantage of the compressibility of the model perturbation in the curvelet domain and to work on series of smaller subproblems each involving a small ran- domized subset of data. We try two different subset sampling strategies, artificial randomized simultaneous sources experiments ("supershots") and drawing sequential shots firing at random source locations. These subsets are changed after each subproblem is solved. In both cases we obtain good migration results at significantly reduced computational cost. Application of these methods to a complicated synthetic model yields to encouraging results, underlining the usefulness of sparsity promotion and randomization in time stepping formulation.},
	keywords = {Compressive Sensing, sparsity promotion, Stochastic optimization, Wave equation migration},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2014/tschannen2014tdl/tschannen2014tdl.pdf},
	author = {Valentin Tschannen and Zhilong Fang and Felix J. Herrmann}
}
@techreport {tang09TRdtr,
	title = {Design of two-dimensional randomized sampling schemes for curvelet-based sparsity-promoting seismic data recovery},
	number = {TR-EOAS-2009-3},
	year = {2009},
	institution = {UBC Earth and Ocean Sciences Department},
	abstract = {The tasks of sampling, compression and reconstruction are very common and often necessary in seismic data processing due to the large size of seismic data. Curvelet-based Recovery by Sparsity-promoting Inversion, motivated by the newly developed theory of compressive sensing, is among the best recovery strategies for seismic data. The incomplete data input to this curvelet-based recovery is determined by randomized sampling of the original complete data. Unlike usual regular undersampling, randomized sampling can convert aliases to easy-to-eliminate noise, thus facilitating the process of reconstruction of the complete data from the incomplete data. Randomized sampling methods such as jittered sampling have been developed in the past that are suitable for curvelet-based recovery, however most have only been applied to sampling in one dimension. Considering that seismic datasets are usually higher dimensional and extremely large, in the present paper, we extend the 1D version of jittered sampling to two dimensions, both with underlying Cartesian and hexagonal grids. We also study separable and non-separable two dimensional jittered sampling, the former referring to the Kronecker product of two one-dimensional jittered samplings. These different categories of jittered sampling are compared against one another in terms of signal-to-noise ratio and visual quality, from which we find that jittered hexagonal sampling is better than jittered Cartesian sampling, while fully non-separable jittered sampling is better than separable sampling. Because in the image processing and computer graphics literature, sampling patterns with blue-noise spectra are found to be ideal to avoid aliasing, we also introduce two other randomized sampling methods, possessing sampling spectra with beneficial blue noise characteristics, Poisson Disk sampling and Farthest Point sampling. We compare these methods, and apply the introduced sampling methodologies to higher dimensional curvelet-based reconstruction. These sampling schemes are shown to lead to better results from CRSI compared to the other more traditional sampling protocols, e.g. regular subsampling.},
	url = {https://slim.gatech.edu/Publications/Public/Journals/2009/tang09TRdtr/tang09TRdtr.pdf},
	author = {Gang Tang and Reza Shahidi and Jianwei Ma}
}
