@presentation {yang2015SINBADFfis,
	title = {Fast imaging with surface-related multiples: {\textendash} shallow water multiples},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we will show surface-related multiples imaging with source estimation. Here the algorithms are based on linearized bregman, and we give the source estimation formulation in this case. With the featured blocked structure of linearized bregman, we can do efficient imaging in straightforward way by randomly choosing sources and frequencies. Another benefit is the simplicity to delivery the algorithm.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/yang2015SINBADFfis/yang2015SINBADFfis.mov},
	author = {Mengmeng Yang and Ning Tu and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADrdc,
	title = {Recent developments in compressive sensing for time-lapse studies},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an overview of the advances we have made with respect to extending compressive sensing ideas to time-lapse studies. We show applications in compressive time-lapse seismic data acquisition, imaging and full-waveform inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}
@presentation {petrenko2014SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2014},
	publisher = {SINBAD},
	abstract = {An implementation of seismic wave simulation on a platform consisting of a conventional host processor and a reconfigurable hardware accelerator is presented. This research is important in the field of exploration for oil and gas resources, where a 3D model of the subsurface is frequently required. By comparing seismic data collected in a real-world survey with synthetic data generated by simulated waves, it is possible to deduce such a model. However this requires many time-consuming simulations with different Earth models to find the one that best fits the measured data. Speeding up the wave simulations would allow more models to be tried, yielding a more accurate estimate of the subsurface. The reconfigurable hardware accelerator employed in this work is a field programmable gate array (FPGA). FPGAs are computer chips that consist of electronic building blocks that the user can configure and reconfigure to represent their algorithm in hardware. Whereas a traditional processor can be viewed as a pipeline for processing instructions, an FPGA is a pipeline for processing data. The chief advantage of the FPGA is that all the instructions in the algorithm are already hardwired onto the chip. This means that execution time depends only on the amount of data to be processed, and not on the complexity of the algorithm. The main contribution is an implementation of the well-known Kaczmarz row projection algorithm on the FPGA, using techniques of dataflow programming. This kernel is used as the preconditioning step of CGMN, a modified version of the conjugate gradients method that is used to solve the time-harmonic acoustic isotropic constant density wave equation. Using one FPGA accelerator, the current implementation allows seismic wave simulations to be performed over twice as fast, compared to running on one Intel Xeon E5-2670 core. I also discuss the effect of modifications of the algorithm necessitated by the hardware on the convergence properties of CGMN. Finally, a specific plan for future work is set-out in order to fully exploit the accelerator platform, and my work is set in its larger context},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/petrenko2014SINBADaih/petrenko2014SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@presentation {kumar2014SINBADeia,
	title = {Extended images in action {\textendash}- image gather with surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Common image gathers are used in building velocity models, invert anisotropy parameters and to analyze reservoir attributes. Often primary reflections are used to form the image gathers and multiples are typically attenuated in processing. However, researchers have shown that, if correctly used, they can provide useful information about the subsurface during reverse-time migration. In this work, i will show how we can use multiples along with primaries to form the image-gathers.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/kumar2014SINBADeia/kumar2014SINBADeia.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {tu2014SINBADfis2,
	title = {Fast imaging with source estimation},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In seismic imaging, an inaccurate estimate of the source wavelet may result in degraded seismic images. While conventional reverse-time migration requires the knowledge of the source wavelet as prior information, we propose to include source estimation in the fast compressive imaging procedure. Using the proposed method, we can obtain seismic images that are comparable to those imaged with the true source wavelet, with computational costs that are comparable to conventional RTM images with all the data. We also extend the proposed method to image data with surface-related multiples, where these multiples help to mitigate the amplitude ambiguity in source estimation. We verify the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis2/tu2014SINBADfis2.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {tu2014SINBADfis,
	title = {Fast imaging with surface-related multiples by sparse inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {When used correctly, surface-related multiples can provide extra illumination coverage compared with primaries. In this talk, I will discuss how to jointly image primaries and surface-related multiples in a computationally efficient fashion. We bring down the computational cost by two means. First we use wave-equation solvers to implicitly carry out the expensive dense matrix-matrix multiplications in the prediction of surface-related multiples. Second we bring down the simulation cost by subsampling the frequencies and monochromatic source experiments together with curvelet-domain sparsity-promoting and rerandomization. As a result, we obtain true-amplitude least-squares migrated seismic images with computational costs that are comparable to a single RTM with all the data. We demonstrate the efficacy of the proposed method using realistic synthetic examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADfis/tu2014SINBADfis.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {tu2014SINBADind,
	title = {Imaging the Nelson data set using surface-related multiples},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We will present out latest finding on migration with multiples on the Nelson dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADind/tu2014SINBADind.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {tu2014SINBADsis,
	title = {Sparse inversion simplified},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Implementation of the SPGl1 solver in low-level computer languages such as C or Fortran may sound daunting to many. This talk is tailored for you if you would like to reap benefits from using sparse constraints to regularize your inversion problem, and at the same time want some easy-to-implement algorithm to solve it. We discuss in this talk the linearized Bregman projection method that embraces both ingredients, and show promising applications in fast least-squares imaging and seismic data interpolation problems.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/tu2014SINBADsis/tu2014SINBADsis.pdf},
	author = {Ning Tu and Navid Ghadermarzy and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {petrenko2013SINBADaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Solution of the Helmholtz equation is the main computational burden of full-waveform inversion in the frequency domain. For this task we employ the CARP-CG algorithm (Gordon \& Gordon 2010), an iterative solver that preconditions the original Helmholtz system into an equivalent symmetric positive definite system and then applies the method of conjugate gradients. Forming the matrix for the new system is not necessary as its multiplicative action on a vector is implemented using a series of projections onto the rows of the original system. Our contribution is implementing CARP-CG for a host + accelerator (FPGA) computing environment. The computational paradigm is one of dataflow: vector and matrix elements are streamed from memory through the accelerator which applies the row projections. The advantage of an FPGA to process streams of data is that unless the algorithm is memory bandwidth limited, computation time is directly proportional to the amount of data. The complexity of the algorithm implemented on the FPGA is irrelevant since all the operations programmed onto the FPGA happen in the same clock tick. In contrast, on a CPU, more complex algorithms require more clock ticks as the instructions are executed sequentially, or with only a small amount of parallelism. Ongoing work porting the CARP-CG algorithm to the accelerator is presented.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/petrenko2013SINBADaih/petrenko2013SINBADaih.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Felix J. Herrmann and Diego Oriato and Simon Tilbury}
}
@presentation {tu2013SINBADrerand,
	title = {Controlling linearization errors with rerandomization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Least squares migration aims to fit the observed seismic data with data predicted by linearized modelling, by solving an PDE-constrained optimization problem. This problem is challenging mostly because of its prohibitive computational cost. To address the issue, dimensionality reduction techniques were proposed in the literature. However, the solution of the reduced problem can deviate from that of the full problem when there are components in the observed data that cannot be explained by linearized modelling. We solve the problem by rerandomizing our $\ell_1$ regularized inversion. In this presentation, I will explain the method and demonstrate what we can achieve with rerandomization, especially for resolving fine sub-salt structures.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADrerand/tu2013SINBADrerand.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {tu2013SINBADfim,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {During this talk, we present a computationally efficient (cost of 1-2 RTMs with all data) iterative sparsity-promoting inversion framework where surface-related multiples are jointly imaged with primaries and where the source signature is estimated on the fly. Our imaging algorithm is computationally efficient because it works during each iteration with small independent randomized subsets of data. The multiples are handled by introducing an areal source term that includes the upgoing wavefield. We update the source signature for each iteration using a variable projection method. The resulting algorithm removes imaging artifacts from surface-related multiples, estimates and removes the imprint of the source, recovers true amplitudes, is fast, and robust to linearization errors by virtue of the statistical independence of the subsets of data we are working with at each iteration.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADfim/tu2013SINBADfim.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {tu2013SINBADvp,
	title = {Fast imaging with multiples and source estimation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Multiples are usually treated as unwanted components in seismic data. However, if correctly used, they can provide valuable information about the subsurface. In this presentation, I will talk about how to make use of multiples in $\ell_1$ regularized least-squares imaging, and how to estimate the source wavelet on the fly using multiples. Synthetic examples show that using multiples not only helps to retrieve the true-amplitude seismic image and the source wavelet, it also increases the wavenumber contents in the seismic image. By using dimensionality reduction techniques with rerandomization, we also greatly decrease the simulation cost without compromising the image quality.},
	keywords = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/tu2013SINBADvp/tu2013SINBADvp.pdf},
	author = {Ning Tu and Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {li2013SINBADmsd,
	title = {Model-space versus data-space FWI with the acoustic wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Inverting data with elastic phases using an acoustic wave equation can lead to erroneous results, especially when the number of iterations is too high, which may lead to over fitting the data. Several approaches have been proposed to address this issue. Most commonly, people data- filtering operations that are aimed to deemphasize the elastic phases in the data in favor of the acoustic phases. Examples of this approach are nested loops over offset range and Laplace parameters. In this presentation, we discuss two complementary optimization-driven methods where the minimization process decides adaptively which of the data or model components are consistent with the objective. Specifically, we compare the s t misfit function as the data-space alternative and curvelet-domain sparsity promotion as the model-space alternative. Application of these two methods to a realistic synthetic lead to comparable results that we believe can be improved by combining these two methods.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/li2013SINBADmsd/li2013SINBADmsd.pdf},
	author = {Xiang Li and Anais Tamalet and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {tu2013SINBADmachar,
	title = {SLIM{\textquoteright}s findings on the Machar dataset},
	year = {2013},
	publisher = {SINBAD},
	abstract = {By courtesy of BP, we are able to get some hands-on experience imaging seismic data from the Machar field in North Sea. We performed reverse-time migration and sparsity-promoting migration with source-estimation to this dataset (or a cropped section of the dataset in many cases due to computational constraints), and had some interesting findings. In this presentation, we will show some conclusive results we have got, and explain the key techniques in imaging this dataset.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/tu2013SINBADmachar/tu2013SINBADmachar.pdf},
	author = {Ning Tu and Felix J. Herrmann}
}
@presentation {ning2012SINBADfim,
	title = {Fast imaging with multiples},
	year = {2012},
	publisher = {SINBAD},
	abstract = {If correctly used, multiple energy can be mapped to the correct subsurface locations. However, simply applying the cross-correlation imaging condition will introduce non-causal artifacts into the final image. Here we propose an inversion approach to image primaries and multiples simultaneously that yields an artifact-free image. To address the high computational cost associated with inversion, we propose to: i) have the wave-equation solver carry out the multi-dimensional convolutions implicitly, and ii) reduce the number of PDE solves by randomized subsampling. We then propose to improve the overall performance of this algorithm by a process called rerandomization, which helps to cancel the correlation built between model iterate and the subsampling operator. We show the merits of our approach on a number of examples.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/ning2012SINBADfim/ning2012SINBADfim_pres.pdf},
	author = {Ning Tu}
}
@presentation {ning2012SINBADSPRINGfim,
	title = {Fast imaging with multiples by sparse inversion},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/ning2012SINBADSPRINGfim/ning2012SINBADSPRINGfim_pres.pdf},
	author = {Ning Tu}
}
@presentation {herrmann2012SINBADfwi,
	title = {Our findings on the Chevron benchmark dataset},
	year = {2012},
	publisher = {SINBAD},
	abstract = {During this presentation, we will review our findings working with the synthetic GOM data released as part of the post SEG workshop:"Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?". This is joint work with Andrew J. Calvert, Ian Hanlon, Mostafa Javanmehri, Rajiv Kumar, Tristan van Leeuwen, Xiang Li, Brendan R. Smithyman, Eric Takam Takougang, and Haneet Wason.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/herrmann2012SINBADfwi/herrmann2012SINBADfwi_pres.pdf},
	author = {Andrew J. Calvert and Ian Hanlon and Mostafa Javanmehri and Rajiv Kumar and Tristan van Leeuwen and Xiang Li and Brendan R. Smithyman and Eric Takam Takougang and Haneet Wason and Felix J. Herrmann}
}
@presentation {tamalet2012SINBADvpe,
	title = {Variance parameters estimation - application to full waveform inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Many inverse problems include nuisance parameters. While not of direct interest, these parameters are required to recover primary parameters. In order to estimate these nuisance parameters as well as the primary parameters in large-scale inverse problems, a method based on variable projection, which consists in projecting out a subset over the variables, has been developed. We present here the application of this method to the problem of variance parameters estimation in multiple datasets, which is an important problem in many areas including geophysics. More precisely, we apply the method to Full Waveform Inversion and demonstrate the improvement in recovery of the model parameters in the case where the variance of the noise increases with the frequency.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/tamalet2012SINBADvpe/tamalet2012SINBADvpe_pres.pdf},
	author = {Anais Tamalet}
}
@presentation {ning2011SINBADmsr,
	title = {Migration from surface-related multiples},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/ning2011SINBADmsr/ning2011SINBADmsr_pres.pdf},
	author = {Ning Tu}
}
@presentation {tu2010SINBADspm,
	title = {Sparsity promoting migration with surface-related multiples},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/tu2010SINBADspm/tu2010SINBADspm_pres.pdf},
	author = {Ning Tu}
}
@conference {thomson2006SINBADlss,
	title = {Large-scale seismic data recovery by the parallel windowed curvelet transform},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear operators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallelprocessing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we have applied to a seismic data interpolation algorithm. Some preliminary results will be shown. Henryk Modzeleweski: Design and specifications for SLIMPy{\textquoteright}s software framework The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development philosophy adopted by SLIM. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/thomson2006SINBADlss/thomson2006SINBADlss.pdf},
	author = {Darren Thomson}
}
@conference {thomson2006SINBADppe,
	title = {(P)SLIMPy: parallel extension},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {The parallel extensions to the SLIMpy environment enable pipe-based processing of large data sets in an MPI-based parallel environment. Parallel processing can be done by straightforward slicing of data, or by using an overlapping domain decomposition that requires communication between different processors. The principal aim of the parallel extensions is to leave abstract numerical algorithms (ANA{\textquoteright}s) and applications programmed for use in SLIMpy untouched when moving to parallel processing. The object-oriented functionality of Python makes this possible.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/thomson2006SINBADppe/thomson2006SINBADppe.pdf},
	author = {Darren Thomson}
}
