@presentation {peters2017SINBADFaaj,
	title = {Algorithms and Julia software for FWI with multiple constraints},
	year = {2017},
	publisher = {SINBAD},
	abstract = {We present a framework to add multiple convex and non-convex constraints to nonlinear inverse problems, specifically FWI. The constraints mitigate problems related to noisy data, artifacts arising from working with very few simultaneous sources, inaccurate starting models and using approximate physical forward models. Compared to earlier work at SLIM, the current framework is algorithmically simpler and computationally more efficient. We show examples where the model estimation is improved when we use very limited prior knowledge directly as constraints. We also present the software implementation in Julia and how it is used together with other software that compute data-misfit values and gradients w.r.t. the model parameters.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/peters2017SINBADFaaj/peters2017SINBADFaaj.m4v},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {bougher2016SINBADFaaa,
	title = {Amplitude vs. angle analysis as an unsupervised learning problem},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Amplitude vs. angle analysis (AVA) of pre-stack seismic data is a commonly used method for inferring petrophysical information from seismic data. Conventionally, a two-term linearized rock physics model (Shuey equation) is used to invert angle-domain common-image gathers. Multivariate analysis of the inverted terms leads to a background of siliciclastic interfaces, where outlying points are associated with hydrocarbon saturated sands. The acquisition and processing of seismic data does not result in highly-calibrated measurements that adhere to the rock physics model, which often inhibits the success of AVA analysis. We offer an alternative approach that uses PCA-based methods to learn projections directly from the data without the need of a physical model. Results on synthetic and field data show that PCA-based projections can improve segmentation of potential reservoirs in seismic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/bougher2016SINBADFaaa/bougher2016SINBADFaaa.mov},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@presentation {oghenekohwo2016SINBADFast,
	title = {Asymmetric sampling for time-lapse surveys},
	year = {2016},
	publisher = {SINBAD},
	abstract = {In this talk, we use the joint recovery model (JRM), which derives from distributed compressed sensing, to reconstruct time-lapse data acquired via time-jittered marine sources that are significantly subsampled in the monitor survey(s) in comparison to the baseline survey, hence, saving acquisition cost. We analyze the effects of two or more asymmetrically subsampled monitor surveys on wavefield reconstruction via JRM. We leverage the common information shared amongst the vintages during our sparsity-promoting recovery.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/oghenekohwo2016SINBADFast/oghenekohwo2016SINBADFast.mov},
	author = {Felix Oghenekohwo and Haneet Wason and Felix J. Herrmann}
}
@presentation {kumar2015SINBADFaoi,
	title = {Affordable omnidirectional image volumes extension to 3D},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Image gathers are an important tool for velocity analysis, AVA analysis and targeted imaging, however, the computational cost involving in forming the image volumes makes them prohibitively expensive in case of large scale 3D seismic data acquisition. In this work, we propose the extension of our probing techniques from 2D to 3D to form the extended image volumes efficiently. We show the efficacy of proposed formulation on synthetic data set.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/kumar2015SINBADFaoi/kumar2015SINBADFaoi.mov},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {witte2015SINBADFara,
	title = {Anisotropic RTM applied to field data},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk we present our latest reverse time-migration results of the BP Machar data set, a 2D seismic line from the North sea featuring a large chalk dome. In contrast to our prior imaging attempts, we now account for anisotropic effects in the data using our new TTI pure quasi-P wave modeling code in the time domain.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/witte2015SINBADFara/witte2015SINBADFara.mov},
	author = {Philipp A. Witte and Felix J. Herrmann}
}
@presentation {esser2015SINBADasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	note = {posthumously presented by Felix J. Herrmann},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/esser2015SINBADasd/esser2015SINBADasd.pdf},
	author = {Ernie Esser and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@presentation {esser2015SINBADFasd,
	title = {Automatic salt delineation {\textemdash} Wavefield Reconstruction Inversion with convex constraints},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We extend full-waveform inversion by Wavefield Reconstruction Inversion by including convex constraints on the model. Contrary to the conventional adjoint-state formulations, Wavefield Reconstruction Inversion has the advantage that the Gauss-Newton Hessian is well approximated by a diagonal scaling, which allows us to add convex constraints, such as the box- and the edge-preserving total-variation constraint, on the square slowness without incurring significant increases in computational costs. As the examples demonstrate, including these constraints yields far superior results in complex geological areas that contain high-velocity high-contrast bodies (e.g. salt or basalt). Without these convex constraints, adjoint-state and Wavefield Reconstruction Inversion get trapped in local minima for poor starting models.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFasd/esser2015SINBADFasd.mov},
	author = {Ernie Esser and Llu{\'\i}s Guasch 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 {warner2014SINBADawi,
	title = {Adaptive waveform inversion - FWI without cycle skipping},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Conventional FWI minimises the direct differences between observed and predicted seismic datasets. Because seismic data are oscillatory, this approach will suffer from the detrimental effects of cycle skipping if the starting model is inaccurate. We reformulate FWI so that it instead adapts the predicted data to the observed data using Wiener filters, and then iterates to improve the model by forcing the Wiener filters towards zero-lag delta functions. This adaptive FWI scheme is demonstrated on synthetic data where it is shown to be immune to cycle skipping, and is able to invert successfully data for which conventional FWI fails entirely. The new method does not require low frequencies or a highly accurate starting model to be successful. Adaptive FWI has some features in common with wave-equation migration velocity analysis, but it works for all types of arrivals including multiples and refractions, and it does not have the high computational costs of WEMVA in 3D.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/warner2014SINBADawi/warner2014SINBADawi.pdf},
	author = {Mike Warner}
}
@presentation {yilmaz2014SINBADadc,
	title = {Analog-to-digital conversion in compressive sampling},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Compressive sampling theory is now established as an effective method for dimension reduction when the underlying signals (e.g., seismic data) are sparse with respect to some suitable basis or frame (e.g., curvelets in the case of seismic). One important problem directly related to the acquisition of analog signals is how to perform analog-to-digital conversion. This is directly related to two important issues: (i) how accurately one can acquire a signal using compressive sampling (in terms of bit depth), and (ii) how {\textquoteleft}{\textquoteleft}compressed{\textquoteright}{\textquoteright} is compressive sampling (in terms of the total number of bits one ends up using after acquiring the signal). We will present recent results that provide answers to these questions. Specifically, we provide an analog-to-digital conversion method that achieves nearly optimal compression using cheap analog devices with a large margin for imperfections.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/yilmaz2014SINBADadc/yilmaz2014SINBADadc.pdf},
	author = {Ozgur Yilmaz}
}
@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 {esser2013SINBADasr,
	title = {Applications of phase retrieval methods to blind seismic deconvolution},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Phase retrieval is the non-convex optimization problem of recovering a signal from magnitudes of complex linear measurements. Solving convex semi-definite program (SDP) relaxations has been shown to be a robust approach, but it remains too expensive to apply to large problems. We will discuss methods for accelerating computations and explore applications to seismic deconvolution.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/esser2013SINBADasr/esser2013SINBADasr.pdf},
	author = {Ernie Esser and Felix J. Herrmann}
}
@presentation {miao2012SINBADasp,
	title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Sparse promoting recovery problem arises more and more frequently with the broad application of compressed sensing tool in exploration seismology. Because of the curse of dimensionality, the prohibitive computation burden on iteratively evaluating objective functions is one of the key issues that constrain high performance l1 solver. In this paper, we try to further improve the convergence performance of SPGl1, one of the state-of-the-art large scale sparse recovery solver, and as a result limit the number of objective function evaluations by introducing a projected quasi Newton method. Examples showing acceleration on seismic data collection, data processing as well as inversion are included.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
	author = {Lina Miao}
}
@presentation {hargreaves2012SINBADavs,
	title = {Analysis versus synthesis in weighted sparse recovery},
	year = {2012},
	publisher = {SINBAD},
	abstract = {The synthesis model for compressive sensing has been the model of choice for many years and various weighting schemes have been shown to improve it{\textquoteright}s performance (see Yilmaz, Mansour, and Ghadermarzy talks). However, there is a counterpart model to synthesis, namely the analysis model, which has been less popular but recently attracted more attention (see Lin{\textquoteright}s talk). In this talk, weighting in the analysis model is discussed and applied to the seismic trace interpolation problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/hargreaves2012SINBADavs/hargreaves2012SINBADavs_pres.pdf},
	author = {Brock Hargreaves}
}
@presentation {warner2012SINBADafw,
	title = {Anisotropic 3D full-waveform inversion of the Tommeliten Alpha field},
	year = {2012},
	publisher = {SINBAD},
	abstract = {We have implemented a robust and practical scheme for anisotropic 3D acoustic full-waveform inversion. We demonstrate this scheme on a field data set, applying it to a four-component ocean-bottom survey over the Tommeliten Alpha field in the North Sea. This shallow-water data set provides good azimuthal coverage to offsets of 7 km, with reduced coverage to a maximum offset of about 11 km. The reservoir lies at the crest of a high-velocity antiformal chalk section, overlain by about 3000 m of clastics within which a low-velocity gas cloud produces a seismic obscured area. We inverted only the hydrophone data, and we retained free-surface multiples and ghosts within the field data. We invert in six narrow frequency bands, in the range 3 to 6.5 Hz. At each iteration, we selected only a subset of sources, using a different subset at each iteration; this strategy is more efficient than inverting all the data every iteration. Our starting velocity model was obtained using standard PSDM model building including anisotropic reflection tomography, and contained epsilon values as high as 20\%. We have also attempted full-elastic inversion of these data to recover a shallow isotropic model of both p and s-wave velocities. The final FWI velocity model shows a network of shallow high-velocity channels that match similar features in the reflection data. Deeper in the section, the FWI velocity model reveals a sharper and more-intense low-velocity region associated with the gas cloud in which low-velocity fingers match the location of gas-filled faults visible in the reflection data. The resulting velocity model provides a better match to well logs, and better flattens common image gathers, than does the starting model. Reverse-time migration, using the FWI velocity model, provides significant uplift to the migrated image, simplifying the planform of the reservoir section at depth. The workflows, inversion strategy, and algorithms that we have used have broad application to invert a wide-range of analogous field data sets.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/warner2012SINBADafw/warner2012SINBADafw_pres.pdf},
	author = {Mike Warner}
}
@presentation {friedlander2010SINBADaso,
	title = {Algorithms for sparse optimization},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/friedlander2010SINBADaso/friedlander2010SINBADaso_pres.pdf},
	author = {Michael P. Friedlander}
}
@conference {herrmann2008SINBADacd2,
	title = {Adaptive curvelet-domain primary-multiple separation},
	booktitle = {SINBAD},
	year = {2008},
	note = {SINBAD 2008},
	publisher = {SINBAD},
	organization = {SINBAD},
	abstract = {In many exploration areas, successful separation of primaries and multiples greatly determines the quality of seismic imaging. Despite major advances made by Surface-Related Multiple Elimination (SRME), amplitude errors in the predicted multiples remain a problem. When these errors vary for each type of multiple differently (as a function of offset, time and dip), these amplitude errors pose a serious challenge for conventional least-squares matching and for the recently introduced separation by curvelet-domain thresholding. We propose a data-adaptive method that corrects amplitude errors, which vary smoothly as a function of location, scale (frequency band) and angle. In that case, the amplitudes can be corrected by an element-wise curvelet-domain scaling of the predicted multiples. We show that this scaling leads to a successful estimation of the primaries, despite amplitude, sign, timing and phase errors in the predicted multiples. Our results on synthetic and real data show distinct improvements over conventional least-squares matching, in terms of better suppression of multiple energy and high-frequency clutter and better recovery of the estimated primaries.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADacd2/herrmann2008SINBADacd2.pdf},
	author = {Felix J. Herrmann}
}
@conference {friedlander2008SINBADafl,
	title = {Algorithms for large-scale sparse reconstruction},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Many signal processing applications seek to approximate a signal as a linear combination of only a few elementary atoms drawn from a large collection. This is known as sparse reconstruction, and the theory of compressed sensing allows us to pose it as a structured convex optimization problem. I will discuss the role of duality in revealing some unexpected and useful properties of these problems, and will show how they can lead to practical, large-scale algorithms. I will also describe some applications of these algorithms.},
	keywords = {SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/friedlander2008SINBADafl/friedlander2008SINBADafl.pdf},
	author = {Michael P. Friedlander}
}
@conference {lebed2008SINBADaoc1,
	title = {Applications of curvelets/surfacelets to seismic data processing},
	booktitle = {SINBAD},
	year = {2008},
	abstract = {In this talk we explore several applications of the curvelet and surfacelet transforms to seismic data processing. The first application is stable signal recovery in the physical domain - seismic data acquisition is often limited by physical and economic constraints, and the goal is to interpolate the data from a given subset of seismic traces. The second application is signal recovery in a transform domain - we assume that our data comes in a form of a random subset of temporal frequencies and the goal is to recover the missing frequencies from this data. Since seismic signals are generally not bandwidth limited, this in fact becomes an anti-aliasing problem. In both these problems the recovery is resolved via a robust $\ell$_1 solver that exploits the sparsity of the signals in curvelet/surfacelet domains. In the last application we explore the problem of primary-multiple separation by simple thresholding.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/lebed2008SINBADaoc1/lebed2008SINBADaoc1.pdf},
	author = {Evgeniy Lebed}
}
