@conference {herrmann2023SIAMCSEtns,
	title = {The Next Step: Interoperable Domain-Specific Programming},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2023},
	note = {(SIAM CSE, Amsterdam)},
	month = {02},
	abstract = {Even though domain-specific programming approaches allow for readable, scalable, and maintainable software without sacrificing performance, the new paradigm of learned physics-informed models calls for an interdisciplinary approach typically involving multiple domain-specific languages. Take for example the problem of inverting for the fluid-flow properties from time-lapse seismic data, which entails domain-specific programming on the intersection of wave simulators, matrix-free linear algebra, learned neural surrogates for two-phase flow, and prior and posterior distributions for the fluid-flow properties. While domain-specific solutions exist for each of these sub-disciplines, integrating these approaches {\textendash} which may involve different programming languages {\textendash} into a single coupled scalable inversion framework that supports algorithmic differentiation can be a challenge. However, we show that challenges like this can be met when working with proper abstractions. In our inversion example, this involves math-inspired symbolic abstractions for numerical solutions of the wave equation (Devito), matrix-free implementations for its Jacobians (JUDI.jl), abstractions for Automatic Differentiation (ChainRules.jl), and homegrown implementations for conditional Invertible Neural Networks (InvertibleNetworks.jl) and Fourier Neural Operators (ParametricOperators.jl).},
	keywords = {algorithms, deep learning, devito, end-to-end, Fourier neural operators, GCS, JUDI, Jutul, SIAM, software, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2023/herrmann2023SIAMCSEtns/index.html},
	author = {Felix J. Herrmann and Mathias Louboutin and Thomas J. Grady II and Ziyi Yin and Rishi Khan}
}
@conference {zhang2022SEGass,
	title = {A simulation-free seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2022-3751690.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/index.html},
	software = {https://github.com/slimgroup/opt_spectral_gap},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {siahkoohi2022INTERSPEECHulbs,
	title = {Ultra-Low-Bitrate Speech Coding with Pretrained Transformers},
	booktitle = {Proceedings of INTERSPEECH},
	year = {2022},
	note = {International Speech Communication Association (ISCA)},
	month = {06},
	abstract = {Speech coding facilitates the transmission of speech over low-bandwidth networks with minimal distortion. Neural-network based speech codecs have recently demonstrated significant improvements in quality over traditional approaches. While this new generation of codecs is capable of synthesizing high-fidelity speech, their use of recurrent or convolutional layers often restricts their effective receptive fields, which prevents them from compressing speech efficiently. We propose to further reduce the bitrate of neural speech codecs through the use of pretrained Transformers, capable of exploiting long-range dependencies in the input signal due to their inductive bias. As such, we use a pretrained Transformer in tandem with a convolutional encoder, which is trained end-to-end with a quantizer and a generative adversarial net decoder. Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of 600 bps that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate. Subjective human evaluations suggest that the quality of the resulting codec is comparable or better than that of conventional codecs operating at three to four times the rate.},
	keywords = {deep learning, GANs, INTERSPEECH, signal processing, Transformers},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/INTERSPEECH/2022/siahkoohi2022INTERSPEECHulbs/paper.pdf},
	author = {Ali Siahkoohi and Michael Chinen and Tom Denton and W. Bastiaan Kleijn and Jan Skoglund}
}
@conference {kumar2021SEGeuq,
	title = {Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF){\textemdash}an interpolation example},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {1515-1519},
	abstract = {Seismic data go through a sequence of pre-processing steps before being made into an image. Although some work has been done to assess the uncertainties in the final images, how the uncertainty in the pre-processing affects the results remains largely unexplored. We use Normalizing Flows (NF), a type of deep neural network, to interpolate seismic data and quantify the associated uncertainty. A big advantage of NFs, over the more commonly used Markov Chain Monte Carlo methods, is that they can successfully sample a complex and high-dimensional probability space with fewer assumptions. We present the first application of NF in interpolating (synthetic) seismic data. The statistical measurements retrieved from the network can be used to better characterize the data as it is passed to the post-processing phase.},
	keywords = {deep learning, normalizing flow, SEG, wavefield reconstruction},
	doi = {10.1190/segam2021-3583705.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/kumar2021SEGeuq/kumar2021SEGeuq.pdf},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Rajiv Kumar and Maria Kotsi and Ali Siahkoohi and Alison Malcolm}
}
@conference {bisbas2020IPDPStbf,
	title = {Temporal blocking of finite-difference stencil operators with sparse "off-the-grid" sources},
	booktitle = {IEEE International Parallel and Distributed Processing Symposium},
	year = {2021},
	note = {(IPDPS, virtual)},
	month = {5},
	abstract = {Stencil kernels dominate a range of scientific applications including seismic and medical imaging, image processing, and neural networks. Temporal blocking is a performance optimisation that aims to reduce the required memory bandwidth of stencil computations by re-using data from the cache for multiple time steps. It has already been shown to be beneficial for this class of algorithms. However, optimising stencils for practical applications remains challenging. These computations often include sparsely located operators, not aligned with the computational grid ("off-the-grid"). For example, our work is motivated by sources that inject a wavefield and measurements interpolating grid values. The resulting data dependencies make the adoption of temporal blocking much more challenging. We propose a methodology to inspect these data dependencies and reorder the computation, leading to performance gains in stencil codes where temporal blocking has not been applicable. We implement this novel scheme in the Devito domain-specific compiler toolchain. Devito implements a domain-specific language embedded in Python to generate optimised partial differential equation solvers using the finite-difference method from high-level symbolic problem definitions. We evaluate our scheme using isotropic acoustic, anisotropic acoustic and isotropic elastic wave propagators of industrial significance. Performance evaluation, after auto-tuning, shows that this enables substantial performance improvement through temporal blocking, over highly-optimised vectorized spatially-blocked code of up to 1.6x.},
	keywords = {finite-difference, HPC, performance, wave-equation},
	doi = {10.1109/IPDPS49936.2021.00058},
	url = {https://arxiv.org/pdf/2010.10248.pdf},
	author = {George Bisbas and Fabio Luporini and Mathias Louboutin and Rhodri Nelson and Gerard Gorman and Paul H. J. Kelly}
}
@conference {herrmann2019EAGEHPCaii,
	title = {Accelerating ideation and innovation cheaply in the Cloud the power of abstraction, collaboration and reproducibility},
	booktitle = {4th EAGE Workshop on High-performance Computing},
	year = {2019},
	note = {(EAGE HPC Workshop, Dubai)},
	month = {10},
	keywords = {cloud, devito, EAGE, HPC, JUDI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGEHPC/2019/herrmann2019EAGEHPCaii/herrmann2019EAGEHPCaii_pres.pdf},
	author = {Felix J. Herrmann and Charles Jones and Gerard Gorman and Jan H{\"u}ckelheim and Keegan Lensink and Paul H. J. Kelly and Navjot Kukreja and Henryk Modzelewski and Michael Lange and Mathias Louboutin and Fabio Luporini and James Selvages and Philipp A. Witte}
}
@conference {siahkoohi2019SEGdlb,
	title = {Deep-learning based ocean bottom seismic wavefield recovery},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {2232-2237},
	abstract = {Ocean bottom surveys usually suffer from having very sparse receivers. Assuming a desirable source sampling, achievable by existing methods such as (simultaneous-source) randomized marine acquisition, we propose a deep-learning based scheme to bring the receivers to the same spatial grid as sources using a convolutional neural network. By exploiting source-receiver reciprocity, we construct training pairs by artificially subsampling the fully-sampled single-receiver frequency slices using a random training mask and later, we deploy the trained neural network to fill-in the gaps in single-source frequency slices. Our experiments show that a random training mask is essential for successful wavefield recovery, even when receivers are on a periodic gird. No external training data is required and experiments on a 3D synthetic data set demonstrate that we are able to recover receivers for up to 90 \% missing receivers, missing either randomly or periodically, with a better recovery for random case, at low to midrange frequencies.},
	keywords = {machine learning, obn, reciprocity, reconstruction, SEG},
	doi = {10.1190/segam2019-3216632.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGdlb/siahkoohi2019SEGdlb.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGdlb/siahkoohi2019SEGdlb_pres.pdf},
	author = {Ali Siahkoohi and Rajiv Kumar and Felix J. Herrmann}
}
@conference {yang2019SEGlrr,
	title = {Low-rank representation of subsurface extended image volumes with power iterations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {4470-4474},
	abstract = {Full subsurface extended image volumes (EIVs) contain abundant information such as subsurface image gathers used in imaging, interpretation of rock properties or velocity analysis, but are expensive in term of computational cost and storage requirement due to their large size. Yet, due to their redundant information, monochromatic EIVs exhibit a low-rank structure that allows us to get a good approximation at a computational cost proportional to the rank k , while conventional techniques require at least the number of sources. Such low-rank factorized EIVs are computed thanks to the randomized singular values decomposition (SVD) algorithm. However, recent developments on low-rank approximations of EIVs raise two major questions. First, monochromatic EIVs rely on time-harmonic wave equation solvers, which do not scale well to realistic 3D models. Second, the rank of the monochromatic EIVs increases with the frequency, which yields increasing computational costs and storage. Here, we propose a construction approach based on a time-domain finite-difference wave-equation solver with time stepping, which is combined to power iteration schemes in the randomized SVD algorithm to accelerate the decay of the singular values. We compare the performances of simultaneous iterations, block-Krylov iterations and the original randomized SVD on the computation of the EIV for a small section of the Marmousi model. Then, we show further results on the full Marmousi model to validate our approach.},
	keywords = {full extended image volumes, power iterations, randomized SVD, SEG, time domain},
	doi = {10.1190/segam2019-3215961.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/yang2019SEGlrr/yang2019SEGlrr_pres.pdf},
	author = {Mengmeng Yang and Marie Graff and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2018EAGEasa,
	title = {Automatic statics and residual statics correction with low-rank approximation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {Seismic data, mainly on land, suffers from long-wavelength statics due to the laterally varying and heterogeneous nature of the near-surface weathering layers. We propose an automatic, data-driven and computationally efficient statics correction method based on low-rank approximation to correct for such statics. The method does not require a model to estimate static time shifts, which is the case for other static correction methods; rather it applies the appropriate static corrections on the data such that it becomes low rank in a certain domain. As of now, the method is applicable to data that has been corrected for elevation statics. Due to the near-surface irregularities and due to approximations used by static correction methods that lead to not fully correcting for statics, an iterative residual statics correction becomes necessary. Our proposed method corrects for residual statics without the necessity of the surface consistency assumption and a multi-iterate process. Additional benefits of the method include artifacts and noise suppression. We demonstrate the successful application of our method on several synthetic data examples.},
	keywords = {EAGE, land, Rank, residual statics, statics},
	doi = {10.3997/2214-4609.201801107},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kumar2018EAGEcsb,
	title = {Compressed sensing based land simultaneous acquisition using encoded sweeps},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {Simultaneous shooting methods using encoded sweeps can enhance the productivity of land acquisition in situations where deployment of many vibrators and larger receiver spread is not possible in the field due to obstructions or permit limitations. However, the existing framework requires shooting the full sequence of encoded sweeps on each shot point to reconstruct the complete frequency bandwidth Green{\textquoteright}s function. Although this simultaneous shooting method reduces the sweeping time vs conventional sequential shooting, the gain in efficiency is limited. To further reduce the sweeping time, we propose to acquire randomly selected subsets of the encoded sweeps sequences followed by a rank-minimization based joint source separation and spectral interpolation framework to reconstruct the full bandwidth deblended Green{\textquoteright}s function. We demonstrate the advantages of proposed sampling and reconstruction framework using a synthetic seismic line simulated using SEG-SEAM Phase II land velocity and density model.},
	keywords = {compressed sensing, EAGE, land, SEAM, source separation, spectral interpolation},
	doi = {10.3997/2214-4609.201800643},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/kumar2018EAGEcsb/kumar2018EAGEcsb.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/kumar2018EAGEcsb/kumar2018EAGEcsb_pres.pdf},
	author = {Rajiv Kumar and Shashin Sharan and Nick Moldoveanu and Felix J. Herrmann}
}
@conference {moldoveanu2018SEGcsbdl,
	title = {Compressive sensing based design for land and OBS surveys: The noise issue},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {102-106},
	abstract = {Two important developments in seismic acquisition in the last decade were introduction of simultaneous shooting and reconstruction via inversion of the complete seismic wavefield. As the seismic wavefield is typically under sampled in at least one of the four spatial coordinates, both developments could contribute to improve the sampling and, in addition, to increase acquisition efficiency. Herein, we look at the impact of seismic noise when simultaneous shooting and seismic wavefield reconstruction are implemented for a land or ocean bottom seismic survey.},
	keywords = {SEG},
	doi = {10.1190/segam2018-2997759.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/moldoveanu2018SEGcsbdl/moldoveanu2018SEGcsbdl.pdf},
	author = {Nick Moldoveanu and Philip Bilsby and John Quigley and Rajiv Kumar and Felix J. Herrmann}
}
@conference {sharan2018SEGada,
	title = {A debiasing approach to microseismic},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {2942-2946},
	abstract = {Microseismic data is often used to locate fracture locations and their origin in time created by fracking. Although surface microseismic data can have large apertures and is easier to acquire than the borehole data, it often suffers from poor signal to noise ratio (S/R). Poor S/R poses a challenge in terms of estimating the correct location and source-time function of a microseismic source. In this work, we propose a denoising step in combination with a computationally cheap debiasing based approach to locate microseismic sources and to estimate their source-time functions with correct amplitude from extremely noisy data. Through numerical experiments, we demonstrate that our method can work with closely spaced microseismic sources with source-time functions of different peak amplitudes and frequencies. We have also shown the ability of our method with the smooth velocity model.},
	keywords = {Full-waveform inversion, Imaging, microseismic, noise, SEG, wave equation},
	doi = {10.1190/segam2018-2997252.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@conference {kadu2018EAGEdfwi,
	title = {Decentralized full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {With the advent of efficient seismic data acquisition, we are having a surplus of seismic data, which is improving the imaging of the earth using full-waveform inversion. However, such inversion suffers from many issues, including (i) substantial network waiting time due to repeated communications of function and gradient values in the distributed environment, and (ii) requirement of the sophisticated optimizer to solve an optimization problem involving non-smooth regularizers. To circumvent these issues, we propose a decentralized full-waveform inversion, a scheme where connected agents in a network optimize their objectives locally while being in consensus. The proposed formulation can be solved using the ADMM method efficiently. We demonstrate using the standard marmousi model that such scheme can decouple the regularization from data fitting and reduce the network waiting time.},
	keywords = {EAGE},
	doi = {10.3997/2214-4609.201801230},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/kadu2018EAGEdfwi/kadu2018EAGEdfwi.pdf},
	author = {Ajinkya Kadu and Rajiv Kumar}
}
@conference {siahkoohi2018SEGcnn,
	title = {Deep Convolutional Neural Networks in prestack seismic{\textendash}-two exploratory examples},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {2196-2200},
	abstract = {Deep convolutional neural networks are having quite an impact and have resulted in step changes in the capabilities of image and voice recognition systems and there are indications they may have a similar impact on post-stack seismic images. While these developments are certainly important, we are after all dealing with an imaging problem involving an unknown earth and not completely understood physics. When the imaging step itself is not handled properly, this may possibly offset gains offered by deep learning. Motivated by our work on deep convolutional neural networks in seismic data reconstruction, we discuss how generative networks can be employed in prestack problems ranging from the relatively mondane removal of the effects of the free surface to dealing with the complex effects of numerical dispersion in time-domain finite differences. While the results we present are preliminary, there are strong indications that generative deep convolutional neural networks can have a major impact on complex tasks in prestack seismic data processing and modeling for inversion.},
	keywords = {Dispersion, machine learning, Modeling, Processing, SEG, SRME},
	doi = {10.1190/segam2018-2998599.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/siahkoohi2018SEGcnn/siahkoohi2018SEGcnn.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/siahkoohi2018SEGcnn/siahkoohi2018SEGcnn_pres.pdf},
	author = {Ali Siahkoohi and Mathias Louboutin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kadu2018SEGMSFWI,
	title = {Full-waveform inversion with Mumford-Shah regularization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {1258-1262},
	abstract = {Full-waveform inversion (FWI) is a non-linear procedure to es- timate subsurface rock parameters from surface measurements of induced seismic waves. This procedure is ill-posed in nature and hence, requires regularization to enhance some structure depending on the prior information. Recently, Total-variation (TV) regularization has gained popularity due to its ability to produce blocky structures. Contrary to this, the earth behave more like a piecewise smooth function. TV regularization fails to enforce this prior information into FWI. We propose a Mumford-Shah functional to incorporate the piecewise smooth spatial structure in the FWI procedure. The resulting opti- mization problem is solved by a splitting method. We show the improvement in results against TV regularization on two synthetic camembert examples.},
	keywords = {FWI, Mumford-Shah, regularization, SEG},
	doi = {10.1190/segam2018-2997224.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/kadu2018SEGMSFWI/kadu2018SEGMSFWI.pdf},
	author = {Ajinkya Kadu and Rajiv Kumar and Tristan van Leeuwen}
}
@conference {kumar2018SEGlreiv,
	title = {Low-rank representation of extended image volumes{\textendash}{\textendash}applications to imaging and velocity continuation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4418-4422},
	abstract = {In this paper, we present a cost-effective low-rank factorized form of computing the full subsurface extended image volumes without explicitly calculating the receiver wavefields. The propose approach is computationally feasible, which exploits the low-rank structure of full subsurface extended image volumes organized as a matrix, thus avoiding the customary loop over sources. Using carefully selected stylized examples, we show how conventional migration images can be extracted from the low-rank factorized form. We also present a velocity continuation procedure that uses the same low-rank form and allows us to map the extended image for one velocity model to another velocity model without recomputing all the source wavefields.},
	keywords = {image volume, low-rank, SEG, velocity continuation},
	doi = {10.1190/segam2018-2998404.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/kumar2018SEGlreiv/kumar2018SEGlreiv.html},
	author = {Rajiv Kumar and Marie Graff-Kray and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2018SMCtpa,
	title = {The power of abstraction in Computational Exploration Seismology},
	booktitle = {Smoky Mountains Computational Sciences and Engineering Conference},
	year = {2018},
	month = {08},
	abstract = {The field of Computational Exploration Seismology has over the years benefited tremendously from developments in HPC. Back in the 80{\textquoteright}s and early 90{\textquoteright}s, the oil \& gas industry was among the main drivers of HPC technology with a resurgence about ten years ago with the advent of full-waveform inversion-i.e., an adjoint-state method for inverse problems that involve the wave equation. While the combination of hardware developments and tedious manual coding efforts have resulted in major improvements, the rate of innovations is slow in part because of the inherent complexities of the mostly monolithic code bases. As a result, innovations make it too slowly into these optimized codes. This stifles innovation and could prevent adaptation of important developments such as machine learning with deep convolutional neural networks into existing workflows. In comparison, the technological advances in the academic as well as commercial machine learning communities have been much faster in part because of robust and well abstracted code bases such as PyTorch and TensorFlow. Since both machine learning and adjoint-state methods rely on back propagation strong similarities exist ready to be exploited. During my talk, I will discuss lessons we learned in finding the appropriate abstractions, read Domain Specific Language, for time-stepping stencil-based finite differences in Devito, and how this is relevant for machine learning with deep networks. I will also share a vision on how ideas from machine learning can be merged into the development of the next-generation of wave-equation based imaging codes. In particular, I will demonstrate the remarkable ability of deep convolutional networks to map low-fidelity dispersed numerical wave simulations to high-fidelity ones. I feel that this apparently generalizable capability of neural nets opens a complete new approach to tackle fundamental problems in computational exploration seismology.},
	keywords = {SMC},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SMC/2018/herrmann2018SMCtpa/herrmann2018SMCtpa_pres.pdf},
	author = {Felix J. Herrmann and Gerard J. Gorman and Jan H{\"u}ckelheim and Keegan Lensink and Paul H. J. Kelly and Navjot Kukreja and Henryk Modzelewski and Michael Lange and Mathias Louboutin and Fabio Luporini and Ali Siahkoohi and Philipp A. Witte}
}
@conference {yang2018SEGrde,
	title = {Removing density effects in LS-RTM with a low-rank matched filter},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4251-4255},
	abstract = {Least-squares reverse-time migration faces difficulties when it inverts the data containing strong components related to density variation with velocity-only Born modeling operator. The strong density perturbation will be inverted as strong dummy velocity perturbations, which influence the amplitudes and phase of the velocity perturbations in the inverted model. The traditional method is to invert the additional density variations by developing Born operator with respect to both density and velocity or modify the image condition. In this work, we develop a matched-filter based LS-RTM for velocity-only Born modeling operator, which removes the artifacts in the imaging created by the strong density variation. This method doesn{\textquoteright}t call for extra work of finite difference stencil and is more general. In the experiment part, we use a complex discontinuous layered medium with strong density variations at the ocean bottom, and show the efficacy of the propose formulation.},
	keywords = {density effect, Least square, low rank, SEG},
	doi = {10.1190/segam2018-2997814.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde_pres.pdf},
	author = {Mengmeng Yang and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@conference {siahkoohi2018EAGEsdr,
	title = {Seismic data reconstruction with Generative Adversarial Networks},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {A main challenge in seismic imaging is acquiring densely sampled data. Compressed Sensing has provided theoretical foundations upon which desired sampling rate can be achieved by applying a sparsity promoting algorithm on sub-sampled data. The key point in successful recovery is to deploy a randomized sampling scheme. In this paper, we propose a novel deep learning-based method for fast and accurate reconstruction of heavily under-sampled seismic data, regardless of type of sampling. A neural network learns to do reconstruction directly from data via an adversarial process. Once trained, the reconstruction can be done by just feeding the frequency slice with missing data into the neural network. This adaptive nonlinear model makes the algorithm extremely flexible, applicable to data with arbitrarily type of sampling. With the assumption that we have access to training data, the quality of reconstructed slice is superior even for extremely low sampling rate (as low as 10\%) due to the data-driven nature of the method.},
	keywords = {data reconstruction, EAGE, generative adversarial networks, machine learning},
	doi = {10.3997/2214-4609.201801393},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/siahkoohi2018EAGEsdr/siahkoohi2018EAGEsdr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/siahkoohi2018EAGEsdr/siahkoohi2018EAGEsdr_pres.pdf},
	author = {Ali Siahkoohi and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2018SEGswi,
	title = {Seismic waveform inversion using decomposed one-way wavefields},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {1379-1383},
	abstract = {Conventional seismic full waveform inversion (FWI) estimates a velocity model by matching the two-way predicted data with the observed data. The non-linearity of the problem and the complexity of the Earth{\textquoteright}s subsurface, which results in complex wave propagation may lead to unsatisfactory inversion results. Moreover, having inaccurate overburden models, i.e water velocity model in the ocean bottom acquisition setting, may lead to erroneous velocity models. In order to obtain better velocity models, we simplify the FWI problem by decomposing the total two-way observed and predicted wavefields into one-way wavefields at the receiver locations using acoustic wavefield decomposition. We then propose to fit the less-complex one-way wavefield to obtain a velocity model that contains valuable information about the overburden. We use this inverted velocity model as a better initial model for the inversion using the other more-complex one-way wavefield. We demonstrate our proposed algorithm on acoustic non-inverse crime synthetic data produced from the Marmousi2 model. The proposed method provides improved inversion results compared with conventional FWI due to properly accounting for separate model updates from the up- and down-going wavefields.},
	keywords = {decomposition, FWI, one-way, SEG},
	doi = {10.1190/segam2018-2998162.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/alfaraj2018SEGswi/alfaraj2018SEGswi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/alfaraj2018SEGswi/alfaraj2018SEGswi_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {sharan2018IEEEIUSspoa,
	title = {Sparsity-promoting photoacoustic imaging with source estimation},
	booktitle = {2018 IEEE International Ultrasonics Symposium (IUS)},
	year = {2018},
	note = {(IEEE IUS, Kobe)},
	month = {10},
	pages = {206-212},
	abstract = {Photoacoustics has emerged as a high-contrast imaging modality that provides optical absorption maps inside of tissues, therefore complementing morphological information of conventional ultrasound. The laser-generated photoacoustic waves are usually envelope-detected, thus disregarding the specific waveforms generated by each photoabsorber. Here we propose a sparsity-promoting image reconstruction method that allows the estimation of each photoabsorber{\textquoteright}s source-time function. Preliminary studies showed the ability to reconstruct the optical absorption map of an in silico vessel phantom. By using a sparsity-promoting imaging method, absorption maps and source-time functions can still be recovered even in situations where the number of transducers is decreased by a factor of six. Moreover, the recovery is able to attain higher resolution than conventional beamforming methods. Because our method recovers the source-time function of the absorbers, it could potentially also be used to distinguish different types of photoabsorbers, or the degree of aggregation of exogenous agents, under the assumption that these would generate different source-time functions at the moment of laser irradiation.},
	keywords = {Acceleration, estimation, IEEE, Image reconstruction, Phantoms, photoacoustic imaging, source-time function, sparsity promoting inversion, Transducers, wave equation},
	doi = {10.1109/ULTSYM.2018.8580037},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IEEEIUS/2018/sharan2018IEEEIUSspoa/sharan2018IEEEIUSspoa_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Diego S. Dumani and Mathias Louboutin and Rongrong Wang and Stanislav Emelianov and Felix J. Herrmann}
}
@conference {kumar2017EAGEdha,
	title = {Denoising high-amplitude cross-flow noise using curvelet-based stable principle component pursuit},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Removal of high-amplitude cross-flow noise in marine towed-streamer acquisition is of great interest because cross-flow noise hinders the success of subsequent processing (e.g. EPSI) and migration. However, the removal of cross-flow noise is a challenging process because cross-flow noise dominates steep angles and low-frequency components of the signal. As a result, applying a simple high-pass filter can result in a loss of coherent diving waves and reflected energy. We propose a stable curvelet-based principle-component pursuit approach that does not suffer from this shortcoming because it uses angle- and scale-adaptivity of the curvelet transform in combination with the low-rank property of cross-flow noise. As long as the cross-flow noise exhibits low-rank in the curvelet domain, our method successfully separates this signal component from the diving waves and seismic reflectivity, which is well-know to be sparse in the curvelet domain. Experimental results on a common-shot gather extracted from a coil shooting survey in the Gulf of Mexico shows the potential of our approach.},
	keywords = {coil data, cross-flow noise, curvelet, denoising, EAGE, SPCP},
	doi = {10.3997/2214-4609.201701055},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/kumar2017EAGEdha/kumar2017EAGEdha.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/kumar2017EAGEdha/kumar2017EAGEdha_poster.pdf},
	author = {Rajiv Kumar and Nick Moldoveanu and Felix J. Herrmann}
}
@conference {daskalakis2017SEGdds,
	title = {Developments in the direction of solving extremly large problems in Geophysics},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {4375-4378},
	abstract = {Often in exploration Geophysics we are forced to work with extremely large problems. Acquisitions via dense grids of receivers translate into very large mathematical systems. Usually, depending on the acquisition, the size of the matrix of the system to be solved, can be measured in the millions. The proper way to address this problem is by subsampling. Even though subsampling can reduce the computational efforts required, it can not address stability problems caused by preconditioning and/or instrumental response errors. In this abstract, we introduce a modification of the linearized Bregman solver for these large problems that resolves stability issues.},
	keywords = {cycling, least-squares migration, linearized Bregman, SEG, weighted increment},
	doi = {10.1190/segam2017-17795188.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/daskalakis2017SEGdds/daskalakis2017SEGdds.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/daskalakis2017SEGdds/daskalakis2017SEGdds_pres.pdf},
	author = {Emmanouil Daskalakis and Rachel Kuske and Felix J. Herrmann}
}
@conference {kukreja2017SIAMdsm,
	title = {Devito: symbolic math for automated fast finite difference computations},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2017},
	note = {(SIAM Conference on Computational Science and Engineering, Atlanta)},
	month = {02-03},
	keywords = {acoustic, compiler, devito, finite differences, high performance computing, Modelling, SIAM, stencil},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/kukreja2017SIAMdsm/kukreja2017SIAMdsm_pres.mov},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/kukreja2017SIAMdsm/kukreja2017SIAMdsm_pres.pdf},
	author = {Navjot Kukreja and Michael Lange and Mathias Louboutin and Fabio Luporini and Gerard Gorman}
}
@conference {kukreja2016OGHPClsm,
	title = {Leveraging symbolic math for rapid development of applications for seismic modeling},
	booktitle = {OGHPC},
	year = {2017},
	note = {(Oil and Gas HPC Conference, Rice University)},
	month = {03},
	abstract = {Wave propagation kernels are the core of many commonly used algorithms for inverse problems in exploration geophysics. While they are easy to write and analyze for the simplied cases, the code quickly becomes complex when the physics needs to be made more precise or the performance of these codes is to be optimized. Signicant eort is repeated every time new forms of physics need to be implemented, or a new computing platform to be supported. The use of symbolic mathematics as a domain specic language (DSL) for input, combined with automatic generation of high performance code customized for the target hardware platform is a promising approach to maximize code reuse. Devito is a DSL for nite dierence that uses symbolic mathematics to generate optimized code for wave propagation based on a provided wave equation. It enables rapid application development in a eld where the average time spent on development has historically been in weeks and months. The Devito DSL system is completely wrapped within the Python programming language and the fact that the running code is in C is completely transparent, making it simple to include Devito as part of a larger work ow including multiple applications over a large cluster.},
	keywords = {finite differences, HPC, Modelling, OGHPC, time domain},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/OGHPC/2017/kukreja2016OGHPClsm/kukreja2016OGHPClsm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/OGHPC/2017/kukreja2016OGHPClsm/kukreja2016OGHPClsm_pres.pdf},
	author = {Navjot Kukreja and Mathias Louboutin and Michael Lange and Fabio Luporini and Gerard Gorman}
}
@conference {zhang2017SEGmsd,
	title = {Massive 3D seismic data compression and inversion with hierarchical Tucker},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {1347-1352},
	abstract = {Modern-day oil and gas exploration, especially in areas of complex geology such as fault belts and sub-salt areas, is an increasingly expensive and risky endeavour. Typically long-offset and dense sampling seismic data are required for subsequent shot based processing procedures, e.g. wave-equation based inversion (WEI) and surface-related multiple elimination (SRME). However, these strict requirements result in an exponential growth in data volume size and prohibitive demands on computational resources, given the multidimensional nature of the data volumes. Moreover the physical constraints and cost limitations impose restrictions on acquiring fully sampled data. In this work, we propose to invert our large-scale data from a set of subsampled measurements, resulting in an estimate of the true volume in a compressed low-rank tensor format. Rather than expanding the data to its fully-sampled form for later downstream processes, we demonstrate how to use this compressed data directly via on-the-fly common shot or receiver gathers extraction. The combination of massive compression and fast on demand data reconstruction of 3D shot or receiver gathers leads to a substantial reduction in memory costs but with minimal effects on results in the subsequent processing procedures. We demonstrate the effective implementation of our proposed framework on full-waveform inversion on a 3D seismic synthetic data set generated from a Overthrust model.},
	keywords = {3D, FWI, SEG, tensor algebra},
	doi = {10.1190/segam2017-17742951.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd_pres.pdf},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@conference {lange2017SCIPYofd,
	title = {Optimised finite difference computation from symbolic equations},
	booktitle = {Python in Science Conference Proceedings},
	year = {2017},
	note = {(SciPy, Texas)},
	month = {07},
	pages = {89-96},
	abstract = {Domain-specific high-productivity environments are playing an increasingly important role in scientific computing due to the levels of abstraction and automation they provide. In this paper we introduce Devito, an open-source domain-specific framework for solving partial differential equations from symbolic problem definitions by the finite difference method. We highlight the generation and automated execution of highly optimized stencil code from only a few lines of high-level symbolic Python for a set of scientific equations, before exploring the use of Devito operators in seismic inversion problems.},
	keywords = {domain-specific languages, finite difference, symbolic Python},
	url = {http://conference.scipy.org/proceedings/scipy2017/michael_lange.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SciPy/2017/lange2017SCIPYofd/lange2017SCIPYofd_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Fabio Luporini and Mathias Louboutin and Charles Yount and Jan H{\"u}ckelheim and Gerard Gorman}
}
@conference {louboutin2017SIAMras,
	title = {Raising the abstraction to separate concerns: enabling different physics for geophysical exploration},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2017},
	note = {(SIAM Conference on Computational Science and Engineering, Atlanta)},
	month = {02-03},
	abstract = {Full-waveform inversion is a PDE-constrained optimisation problem involving massive amounts of data (petabytes) and large numbers of unknowns (O(10^9)). This well known compute-intensive and data-intensive is extremely challenging for several reasons. First, there is the complexity of having to handle extremely large data volumes with metadata related to experimental details in the field, and the discretization of the unknown earth parameters and approximate physics. Second, reduced or adjoint-state methods call for computationally intensive PDE solves for each source experiment (of which there are thousands) for each iteration of a gradient-based optimization scheme. The talks will give an overview how carefully chosen layers of abstraction can help manage both the complexity and scale of inversion while still achieving the high degree of computational performance required to make full-waveform a practical tool. Specifically, the presentations will focus on domain specific stencil language for time-stepping methods to solve various types of wave equations and on abstracts for large-scale parallel optimization frameworks.},
	keywords = {finite differences, HPC, Modelling, SIAM, time domain},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/louboutin2017SIAMras/louboutin2017SIAMras_pres.mov},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/louboutin2017SIAMras/louboutin2017SIAMras_pres.pdf},
	author = {Mathias Louboutin and Michael Lange and Navjot Kukreja and Fabio Luporini and Felix J. Herrmann and Gerard Gorman}
}
@conference {alfaraj2017SEGrsw,
	title = {Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {2533-2538},
	abstract = {Spatial Nyquist sampling rate, which is proportional to the apparent subsurface velocity, is the key deciding cost factor for conventional seismic acquisition. Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier, which increases the acquisition costs. This is one of the reasons why shear waves are usually not considered in practice. To avoid having the Nyquist criterion as the deciding cost factor and to utilize multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. Each component can then be interpolated separately, followed by elastic decomposition to recover up- and down-going S-waves. Instead, we jointly interpolate and decompose the recorded multicomponent data by solving one sparsity promoting optimization problem. This way we ensure that the relative amplitudes of the multicomponent data is preserved. We compare two sparsifying transforms: the curvelet transform and the frequency-wavenumber transform. Another key cost deciding factor for seismic acquisition is the efficiency of acquiring data. This calls for simultaneous acquisition, which requires a source separation step. Similarly, instead of taking a two-step approach, we perform a sparsity-promoting joint source separation decomposition. Results on economically and efficiently acquired synthetic data of both joint methods show their ability of reconstructing accurate up- and down-going S-waves.},
	keywords = {decomposition, elastic, interpolation, randomized acquisition, SEG, shear wave, source separation, sparse},
	doi = {10.1190/segam2017-17681376.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2017EAGEswr,
	title = {Shear wave reconstruction from low cost randomized acquisition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Shear waves travel in the subsurface at a lower speed compared with compressional waves. Therefore, much finer spatial sampling is required to properly record the shear waves. This leads to higher acquisition costs which are typically avoided by designing surveys geared towards only compressional waves imaging. We propose using randomly under-sampled ocean bottom acquisition for recording both compressional and shear waves. The recorded multicomponent data is then interpolated using an SVD-free low rank interpolation scheme that is feasible for large scale seismic data volumes to obtain finely sampled data. Following that, we perform elastic wavefield decomposition at the ocean bottom to recover accurate up- and dow-going S-waves. Synthetic data results indicate that using randomized under-sampled acquisition, we can recover accurate S-waves with an economical cost compared with conventional acquisition designs.},
	keywords = {EAGE, interpolation, randomized acquisition, rank minimization, shear waves},
	doi = {10.3997/2214-4609.201700594},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/alfaraj2017EAGEswr/alfaraj2017EAGEswr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/alfaraj2017EAGEswr/alfaraj2017EAGEswr_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {fang2017WAVESuqf,
	title = {Uncertainty quantification for inverse problems with a weak wave-equation constraint},
	booktitle = {WAVES 2017 {\textendash}- 13th International Conference on Mathematical and Numerical Aspects of Wave Propagation},
	year = {2017},
	note = {(WAVES, Minneapolis)},
	month = {05},
	pages = {127-128},
	abstract = {In this work, we present a new posterior distribution to quantify uncertainties in solutions of wave-equation based inverse problems. By introducing an auxiliary variable for the wavefields, we weaken the strict wave-equation constraint used by conventional Bayesian approaches. With this weak constraint, the new posterior distribution is a bi-Gaussian distribution with respect to both model parameters and wavefields, which can be directly sampled by the Gibbs sampling method.},
	keywords = {constraint, Gibbs sampling, uncertainty, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/WAVES/2017/fang2017WAVESuqf/fang2017WAVESuqf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/WAVES/2017/fang2017WAVESuqf/fang2017WAVESuqf_pres.pdf},
	author = {Zhilong Fang and Curt Da Silva and Rachel Kuske and Felix J. Herrmann}
}
@conference {herrmann2016SEGWSctl,
	title = {Compressive time-lapse marine acquisition},
	booktitle = {SEG Workshop on Low cost geophysics: How to be creative in a cost-challenged environment; Dallas},
	year = {2016},
	note = {(SEG Workshop, Dallas)},
	month = {10},
	keywords = {Acquisition, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/herrmann2016SEGWSctl/herrmann2016SEGWSctl_pres.pdf},
	author = {Felix J. Herrmann and Rajiv Kumar and Felix Oghenekohwo and Shashin Sharan and Haneet Wason}
}
@conference {kukreja2016WOLFHPCdaf,
	title = {Devito: automated fast finite difference computation},
	booktitle = {WOLFHPC 2016 Workshop (Super Computing)},
	year = {2016},
	note = {(WOLFHPC Workshop (SC16), Utah)},
	month = {11},
	abstract = {Domain specific languages have successfully been used in a variety of fields to cleanly express scientific problems as well as to simplify implementation and performance optimization on different computer architectures. Although a large number of stencil languages are available, finite difference domain specific languages have proved challenging to design because most practical use cases require additional features that fall outside the finite difference abstraction. Inspired by the complexity of real-world seismic imaging problems, we introduce Devito, a domain specific language in which high level equations are expressed using symbolic expressions from the SymPy package. Complex equations are automatically manipulated, optimized, and translated into highly optimized C code that aims to perform comparably or better than hand-tuned code. All this is transparent to users, who only see concise symbolic mathematical expressions.},
	keywords = {acoustic, compiler, finite differences, high performance computing, Modelling, stencil},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/WOLFHPC/kukreja2016WOLFHPCdaf/kukreja2016WOLFHPCdaf.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/WOLFHPC/kukreja2016WOLFHPCdaf/kukreja2016WOLFHPCdaf_pres.pdf},
	author = {Navjot Kukreja and Mathias Louboutin and Felippe Vieira Zacarias and Fabio Luporini and Michael Lange and Gerard Gorman}
}
@conference {deaguiar2016SCdff,
	title = {DeVito: fast finite difference computation},
	booktitle = {Super Computing (SC16)},
	year = {2016},
	note = {(Super Computing, Utah)},
	month = {11},
	abstract = {Seismic imaging, used in energy exploration, is arguably the most compute and data intensive application in the private sector. The commonly used methods involve solving the wave equations numerically using finite difference formulations. Writing optimized code for these applications involves multiple man-years of effort that need to be repeated every time a new development needs to be factored in {\textendash} for every target platform. DeVito is a new tool for performing optimized Finite Difference (FD) computation from high-level symbolic problem definitions. The application developer needs to provide a differential equation in symbolic form. DeVito performs automated code generation and Just-In-Time (JIT) compilation based on this symbolic equation to create and execute highly optimized Finite Difference kernels on multiple computer platforms. DeVito has been designed to be used as part of complex workflows involving data flows across multiple applications over different nodes of a cluster.},
	keywords = {finite differences, Full-waveform inversion, high performance computing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SC/2016/deaguiar2016SCdff/deaguiar2016SCdff_poster.pdf},
	url2 = {http://sc16.supercomputing.org/sc-archive/tech_poster/tech_poster_pages/post155.html},
	author = {Marcos de Aguiar and Gerard Gorman and Felix J. Herrmann and Navjot Kukreja and Michael Lange and Mathias Louboutin and Felippe Vieira Zacarias}
}
@conference {lange2016dtg,
	title = {Devito: Towards a generic finite difference DSL using symbolic python},
	booktitle = {6th Workshop on Python for High-Performance and Scientific Computing},
	year = {2016},
	note = {(PyHPC, Utah)},
	month = {11},
	pages = {67-75},
	abstract = {Domain specific languages (DSL) have been used in a variety of fields to express complex scientific problems in a concise manner and provide automated performance optimization for a range of computational architectures. As such DSLs provide a powerful mechanism to speed up scientific Python computation that goes beyond traditional vectorization and pre-compilation approaches, while allowing domain scientists to build applications within the comforts of the Python software ecosystem. In this paper we present Devito, a new finite difference DSL that provides optimized stencil computation from high-level problem specifications based on symbolic Python expressions. We demonstrate Devito{\textquoteright}s symbolic API and performance advantages over traditional Python acceleration methods before highlighting its use in the scientific context of seismic inversion problems.},
	keywords = {acoustic, finite differences, HPC, inversion, Modelling, python, software optimization},
	doi = {10.1109/PyHPC.2016.9},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/PyHPC/2016/lange2016dtg/lange2016dtg_pres.pdf},
	author = {Michael Lange and Navjot Kukreja and Mathias Louboutin and Fabio Luporini and Felippe Vieira Zacarias and Vincenzo Pandolfo and Paulius Velesko and Paulius Kazakas and Gerard Gorman}
}
@conference {lopez2016CMSogl,
	title = {Off-the-grid low-rank matrix recovery: seismic data reconstruction},
	booktitle = {Canadian Mathematical Society Summer Meeting},
	year = {2016},
	note = {(CMS, Edmonton, Alberta)},
	month = {06},
	abstract = {This talk discusses a modified low-rank matrix recovery work-flow that admits unstructured observations. By incorporating a regularization operator which accurately maps structured data to unstructured data, into the nuclear-norm minimization problem, this approach is able to compensate for data irregularity. Furthermore, by construction this formulation yields output that is supported on a structured grid. Recovery error bounds are established for the methodology with matrix sensing and matrix completion numerical experiments including applications to seismic trace interpolation to demonstrate the potential of the approach.},
	keywords = {CMS, matrix completion, matrix sensing, nonuniform discrete Fourier transform, nuclear-norm, Seismic data Interpolation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CMS/2016/lopez2016CMSogl/lopez2016CMSogl_pres.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {kumar2016SEGtjm,
	title = {Time-jittered marine acquisition{\textendash}-a rank-minimization approach for 5D source separation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {119-123},
	abstract = {Simultaneous source marine acquisition has been recognized as an economic way of improving spatial sampling and speedup acquisition time, where a single- (or multiple-) source vessel fires at jittered source locations and time instances. Consequently, the acquired simultaneous data volume is processed to separate the overlapping shot records resulting in densely sampled data volume. It has been shown in the past that the simultaneous source acquisition design and source separation process can be setup as a compressed sensing problem, where conventional seismic data is reconstructed from simultaneous data via a sparsity-promoting optimization formulation. While the recovery quality of separated data is reasonably well, the recovery process can be computationally expensive due to transform-domain redundancy. In this paper, we present a computationally tractable rank-minimization algorithm to separate simultaneous data volumes. The proposed algorithm is suitable for large-scale seismic data, since it avoids singular-value decompositions and uses a low-rank based factorized formulation instead. Results are illustrated for simulations of simultaneous time-jittered continuous recording for a 3D ocean-bottom cable survey.},
	keywords = {5D, marine, SEG, source separation, time-jittered acquisition},
	doi = {10.1190/segam2016-13878249.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/kumar2016SEGtjm/kumar2016SEGtjm_pres.pdf},
	author = {Rajiv Kumar and Shashin Sharan and Haneet Wason and Felix J. Herrmann}
}
@conference {vanleeuwen2015EAGEafs,
	title = {Affordable full subsurface image volume{\textendash}-an application to WEMVA},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE Workshop on Wave Equation based Migration Velocity Analysis, Madrid)},
	month = {06},
	abstract = {Common image gathers are used in building velocity models, inverting for anisotropy parameters, and analyzing reservoir attributes. In this paper, we offer a new perspective on image gathers, where we glean information from the image volume via efficient matrix-vector products. The proposed formulation make the computation of full subsurface image volume feasible. We illustrate how this matrix-vector product can be used to construct objective functions for automatic MVA.},
	keywords = {EAGE, MVA, randomized trace estimation, wave-equation},
	doi = {10.3997/2214-4609.201413498},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/vanleeuwen2015EAGEafs/vanleeuwen2015EAGEafs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/vanleeuwen2015EAGEafs/vanleeuwen2015EAGEafs_pres.pdf},
	author = {Tristan van Leeuwen and Rajiv Kumar and Felix J. Herrmann}
}
@conference {kumar2015CSEGlse,
	title = {Least-squares extended imaging with surface-related multiples},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Common image gathers are used in building velocity models, inverting anisotropy parameters, and analyzing reservoir attributes. Often primary reflections are used to form image gathers and multiples are typically attenuated in processing to remove strong coherent artifacts generated by multiples that interfere with the imaged reflectors. However, researchers have shown that, if correctly used, multiples can actually provide extra illumination of the subsurface in seismic imaging, especially for delineating the near-surface features. In this work, we borrow ideas from literatures on imaging with surface-related multiples, and apply these ideas to extended imaging. This way we save the massive computation cost in separating multiples from the data before using them during the formation of image gathers. Also, we mitigate the strong coherent artifacts generated by multiples which can send the migration velocity analysis type algorithms in wrong direction. Synthetic examples on a three-layer model show the efficacy of the proposed formulation.},
	keywords = {CSEG, image-gather, surface-related multiples},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/kumar2015CSEGlse/kumar2015CSEGlse.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/kumar2015CSEGlse/kumar2015CSEGlse_pres.pdf},
	author = {Rajiv Kumar and Ning Tu and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {kumar2015EAGEmcu,
	title = {Matrix completion on unstructured grids : 2-D seismic data regularization and interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {Seismic data interpolation via rank-minimization techniques has been recently introduced in the seismic community. All the existing rank-minimization techniques assume the recording locations to be on a regular grid, e.g., sampled periodically, but seismic data are typically irregularly sampled along spatial axes. Other than the irregularity of the sampled grid, we often have missing data. In this paper, we study the effect of grid irregularity to conduct matrix completion on a regular grid for unstructured data. We propose an improvement of existing rank-minimization techniques to do regularization. We also demonstrate that we can perform seismic data regularization and interpolation simultaneously. We illustrate the advantages of the modification using a real seismic line from the Gulf of Suez to obtain high quality results for regularization and interpolation, a key application in exploration geophysics.},
	keywords = {EAGE, interpolation, matrix completion, NFFT, regularization},
	doi = {10.3997/2214-4609.201413448},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/kumar2015EAGEmcu/kumar2015EAGEmcu.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/kumar2015EAGEmcu/kumar2015EAGEmcu_poster.pdf},
	author = {Rajiv Kumar and Oscar Lopez and Ernie Esser and Felix J. Herrmann}
}
@conference {kumar2015IIPFWIrmb,
	title = {Rank minimization based seismic data processing and inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present the low-rank extensions of full-waveform inversion to mitigate the local-minima issues. The purpose formulation is computationally tractable and does not involve any extra computational cost compared to the adjoint-state method.},
	keywords = {FWI, inversion, Processing, rank minimization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/kumar2015IIPFWIrmb/kumar2015IIPFWIrmb_pres.pdf},
	author = {Rajiv Kumar and Curt Da Silva and Oscar Lopez and Aleksandr Y. Aravkin and Hassan Mansour and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@conference {lopez2015EAGErma,
	title = {Rank minimization via alternating optimization: seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {Low-rank matrix completion techniques have recently become an effective tool for seismic trace interpolation problems. In this talk, we consider an alternating optimization scheme for nuclear norm minimization and discuss the applications to large scale wave field reconstruction. By adopting a factorization approach to the rank minimization problem we write our low-rank matrix in bi-linear form, and modify this workflow by alternating our optimization to handle a single matrix factor at a time. This allows for a more tractable procedure that can robustly handle large scale, highly oscillatory and critically subsampled seismic data sets. We demonstrate the potential of this approach with several numerical experiments on a seismic line from the Nelson 2D data set and a frequency slice from the Gulf of Mexico data set.},
	keywords = {EAGE, interpolation, low-rank, matrix completion},
	doi = {10.3997/2214-4609.201413453},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/lopez2015EAGErma/lopez2015EAGErma.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/lopez2015EAGErma/lopez2015EAGErma_poster.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@conference {lopez2015SEGWSsfm,
	title = {SVD-free matrix completion in randomized marine acquisition},
	booktitle = {SEG Workshop on Rank-Reduction and Other Sparse Transform Methods with Application to Data Reconstruction, De-Noising, De-Blending and Imaging; New Orleans},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	abstract = {During this presentation, we will illustrate how insights from matrix-free matrix completion can be used to solve problems in (on- and off-the-grid) missing trace interpolation and time-jittered (simultaneous) marine acquisition. In particular, we will demonstrate the importance of data organization to favor recovery by rank minimization and the use of deterministic spectral properties to predict the recovery. We also make make comparisons between wavefield reconstructions based on transform-domain sparsity promotion and matrix completion.},
	keywords = {Acquisition, interpolation, matrix completion, rank reduction, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/lopez2015SEGWSsfm/lopez2015SEGWSsfm_pres.pdf},
	author = {Oscar Lopez and Haneet Wason and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@conference {oghenekohwo2015IIPFWItlf,
	title = {Time-lapse FWI with distributed compressed sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this talk, I will discuss our most recent application of full-waveform inversion (FWI) to time-lapse seismic data inversion. Specifically, I will illustrate how a joint recovery model (JRM) can be used in a joint inversion framework, leveraging ideas from distributed compressed sensing (DCS). The key idea involves exploiting the common information in the baseline and monitor gradients during the inversion.},
	keywords = {distributed compressed sensing, FWI, time lapse},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/oghenekohwo2015IIPFWItlf/oghenekohwo2015IIPFWItlf_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {fang2015IIPFWIuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this work, we propose a method to quantify the uncertainty of wavefield reconstruction inversion under the framework of Bayesian inference. Unlike the conventional method using the wave equation as the forward mapping, we involve the wave equation misfit in the posterior distribution and propose a new posterior distribution. The negative log-likelihood of the new distribution is less oscillatory than that of the conventional posterior distribution, and its Gauss-Newton Hessian is a diagonal matrix that can be generated without any additional computational cost. We use the diagonal Gauss-Newton Hessian to derive an approximate Gaussian distribution at the maximum likelihood point to quantify the uncertainty. This method makes the uncertainty quantification for WRI computationally tractable and is able to provide reasonable uncertainty analysis based on our numerical results.},
	keywords = {FWI, uncertainty, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/fang2015IIPFWIuqw/fang2015IIPFWIuqw_pres.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@conference {fang2015EAGEuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {In this work, we propose a method to quantify the uncertainty of wavefield reconstruction inversion under the framework of Bayesian inference. Unlike the conventional method using the wave equation as the forward mapping, we involve the wave equation misfit in the posterior distribution and propose a new posterior distribution. The negative log-likelihood of the new distribution is less oscillatory than that of the conventional posterior distribution, and its Gauss-Newton Hessian is a diagonal matrix that can be generated without any additional computational cost. We use the diagonal Gauss-Newton Hessian to derive an approximate Gaussian distribution at the maximum likelihood point to quantify the uncertainty. This method makes the uncertainty quantification for WRI computationally tractable and is able to provide reasonable uncertainty analysis based on our numerical results.},
	keywords = {EAGE, FWI, UQ, WRI},
	doi = {10.3997/2214-4609.201413198},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/fang2015EAGEuqw/fang2015EAGEuqw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/fang2015EAGEuqw/fang2015EAGEuqw_pres.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@conference {oghenekohwo2015EAGEuci,
	title = {Using common information in compressive time-lapse full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The use of time-lapse seismic data to monitor changes in the subsurface has become standard practice in industry. In addition, full-waveform inversion has also been extended to time-lapse seismic to obtain useful time-lapse information. The computational cost of this method are becoming more pronounced as the volume of data increases. Therefore, it is necessary to develop fast inversion algorithms that can also give improved time-lapse results. Rather than following existing joint inversion algorithms, we are motivated by a joint recovery model which exploits the common information among the baseline and monitor data. We propose a joint inversion framework, leveraging ideas from distributed compressive sensing and the modified Gauss-Newton method for full-waveform inversion, by using the shared information in the time-lapse data. Our results on a realistic synthetic example highlight the benefits of our joint inversion approach over a parallel inversion method that does not exploit the shared information. Preliminary results also indicate that our formulation can address time-lapse data with inconsistent acquisition geometries.},
	keywords = {EAGE, FWI, time-lapse},
	doi = {10.3997/2214-4609.201413086},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci_poster.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {kumar2014EAGEeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. In this paper, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points into a matrix whose (i,j)^th entry captures the interaction between gridpoints i and j. For even small problems, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via efficient matrix-vector products. We illustrate how this can be used to construct objective functions for automatic MVA.},
	keywords = {EAGE, extended imaging, MVA, probing},
	doi = {10.3997/2214-4609.20141492},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia_pres.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {oghenekohwo2014SEGrsw,
	title = {Randomized sampling without repetition in time-lapse surveys},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4848-4852},
	abstract = {Vouching for higher levels of repeatability in acquisition and processing of time-lapse (4D) seismic data has become the standard with oil and gas contractor companies, with significant investment in the design of acquisition systems and processing algorithms that attempt to address some of the current 4D challenges, in particular, imaging weak 4D signals. Recent developments from the field of compressive sensing have shown the benefits of variants of randomized sampling in marine seismic acquisition and its impact for the future of seismic exploration. Following these developments, we show that the requirement for accurate survey repetition in time-lapse seismic data acquisition can be waived provided we solve a sparsity-promoting convex optimization program that makes use of the shared component between the baseline and monitor data. By setting up a framework for inversion of the stacked sections of a time-lapse data, given the pre-stack data volumes, we are able to extract 4D signals with relatively highfidelity from significant subsamplings. Our formulation is applied to time-lapse data that has been acquired with different source/receiver geometries, paving the way for an efficient approach to dealing with time-lapse data acquired with initially poor repeatability levels, provided the survey geometry details are known afterwards.},
	keywords = {4D, Acquisition, random, repetition, SEG, time-lapse},
	doi = {http://dx.doi.org/10.1190/segam2014-1627.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Felix J. Herrmann}
}
@conference {wason2014SEGsss,
	title = {Source separation via SVD-free rank minimization in the hierarchical semi-separable representation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {120-126},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of source separation or deblending. In this paper, we propose a source separation algorithm for blended marine acquisition, where two sources are deployed at different depths (over/under acquisition). The separation method incorporates the Hierarchical Semi-Separable structure (HSS) inside rank-regularized least-squares formulations. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the performance of the new HSS-based deblending approach by simulating an over/under blended acquisition, wherein uniformly random time delays (of < 1 second) are applied to one of the sources.},
	keywords = {Acquisition, deblending, HSS, marine, Rank, SEG, source separation},
	doi = {http://dx.doi.org/10.1190/segam2014-1583.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGsss/wason2014SEGsss.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGsss/wason2014SEGsss_pres.pdf},
	author = {Haneet Wason and Rajiv Kumar and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@conference {kumar2014EAGErank,
	title = {SVD-free low-rank matrix factorization : wavefield reconstruction via jittered subsampling and reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Recently computationally efficient rank optimization techniques have been studied extensively to develop a new mathematical tool for the seismic data interpolation. So far, matrix completion problems have been discussed where sources are subsample according to a discrete uniform distribution. In this paper, we studied the effect of two different subsampling techniques on seismic data interpolation using rank-regularized formulations, namely jittered subsampling over uniform random subsampling. The other objective of this paper is to combine the fact of source-receiver reciprocity with the rank-minimization techniques to enhance the accuracy of missing-trace interpolation. We illustrate the advantages of jittered subsampling and reciprocity using a seismic line from Gulf of Suez to obtain high quality results for interpolation, a key application in exploration geophysics.},
	keywords = {EAGE, interpolation, low-rank, reciprocity},
	doi = {10.3997/2214-4609.20141394},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGErank/kumar2014EAGErank.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGErank/kumar2014EAGErank_pres.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Ernie Esser and Hassan Mansour and Felix J. Herrmann}
}
@conference {kumar2013SEGAVA,
	title = {AVA analysis and geological dip estimation via two-way wave-equation based extended images},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {423-427},
	abstract = {In this paper, we present an efficient way to compute extended images for all subsurface offsets without explicitly calculating the source and receiver wavefields for all the sources. Because the extended images contain all possible subsurface offsets, we compute the angle-domain image gathers by selecting the subsurface offset that is aligned with the local dip. We also propose a method to compute the local dip information directly from common-image-point gathers. To assess the quality of the angle-domain common-image-points gathers we compute the angle-dependent reflectivity coefficients and compare them with theoretical reflectivity coefficients yielded by the (linearized) Zoeppritz equations for a few synthetic models.},
	keywords = {AVA, dip, extended images, SEG, wave-equation},
	doi = {10.1190/segam2013-1348.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGAVA/kumar2013SEGAVA.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGAVA/kumar2013SEGAVA_pres.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {kumar2013SEGMVA,
	title = {Efficient WEMVA using extended images},
	booktitle = {SEG Workshop on Advances in Model Building, Imaging, and FWI; Houston},
	year = {2013},
	month = {09},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. Here, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points in to a matrix whose (i,j)th entry captures the interaction between gridpoints i and j. For even small problems, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via efficient matrix-vector products. We illustrate how this can be used to construct objective functions for automated MVA.},
	keywords = {inversion, MVA, poster, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGMVA/kumar2013SEGMVA_poster.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {kumar2013SEGHSS,
	title = {Reconstruction of seismic wavefields via low-rank matrix factorization in the hierarchical-separable matrix representation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {3628-3633},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. In this paper, we propose an approach for seismic data interpolation which incorporates the Hierarchical Semi-Separable Structure (HSS) inside rank-regularized least-squares formulations for the missing-trace interpolation problem. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the advantages of the new HSS approach by interpolating a seismic line from the Gulf of Suez and compare the reconstruction with conventional rank minimization.},
	keywords = {HSS, interpolation, SEG},
	doi = {10.1190/segam2013-1165.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGHSS/kumar2013SEGHSS.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGHSS/kumar2013SEGHSS_pres.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@conference {kumar2013EAGEsind,
	title = {Seismic data interpolation and denoising using SVD-free low-rank matrix factorization},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {Recent developments in rank optimization have allowed new approaches for seismic data interpolation and denoising. In this paper, we propose an approach for simultaneous seismic data interpolation and denoising using robust rank-regularized formulations. The proposed approach is suitable for large scale problems, since it avoids SVD computations by using factorized formulations. We illustrate the advantages of the new approach using a seismic line from Gulf of Suez and 5D synthetic seismic data to obtain high quality results for interpolation and denoising, a key application in exploration geophysics.},
	keywords = {denoising, EAGE, interpolation},
	doi = {10.3997/2214-4609.20130388},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/kumar2013EAGEsind/kumar2013EAGEsind.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/kumar2013EAGEsind/kumar2013EAGEsind_pres.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ben Recht and Felix J. Herrmann}
}
@conference {herrmann2012SEGfwi,
	title = {FWI from the West Coasts: lessons learned from "Gulf of Mexico Imaging Challenges: What Can Full Waveform Inversion Achieve?"},
	booktitle = {SEG Workshop on FWI; Las Vegas},
	year = {2012},
	publisher = {SEG},
	organization = {SEG},
	keywords = {FWI, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2012/herrmann2012SEGfwi/herrmann2012SEGfwi_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}
}
@conference {kumar2009SEGins,
	title = {Incoherent noise suppression with curvelet-domain sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3356-3360},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The separation of signal and noise is a key issue in seismic data processing. By noise we refer to the incoherent noise that is present in the data. We use the recently introduced multiscale and multidirectional curvelet transform for suppression of random noise. The curvelet transform decomposes data into directional plane waves that are local in nature. The coherent features of the data occupy the large coefficients in the curvelet domain, whereas the incoherent noise lives in the small coefficients. In other words, signal and noise have minimal overlap in the curvelet domain. This gives us a chance to use curvelets to suppress noise present in data.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255557},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/kumar09SEGins/kumar09SEGins.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/kumar09SEGins/kumar09SEGins_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {kumar2008SINBADcd,
	title = {Curvelet denoising},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The separation of signal and noise is an important issue in seismic data processing. By noise we refer to the incoherent noise which is present in the data. In our case, we showed curvelets concentrate seismic signal energy in few significant coefficients unlike noise energy that is spread all over the coefficients. The sparsity of seismic data in the curvelet domain makes curvelets an ideal choice for separating the noise from the seismic data. In our approach the denoising problem is framed as curvelet-regularized inversion problem. After initial processing, we applied the algorithm to the poststack data and compared our results with conventional wavelet denoising.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar2008SINBADcd/kumar2008SINBADcd.pdf},
	author = {Vishal Kumar}
}
@conference {kumar2008SINBADcrd,
	title = {Curvelet-regularized deconvolution},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The removal of source signature from seismic data is an important step in seismic data processing. The Curvelet transform provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic reflectivity). In this approach the sparseness of reflectivity in Curvelet domain is used as a prior to stabilize the inversion process. Our Curvelet-regularized deconvolution algorithm uses recently developed SPGL1 solver which does adaptive sampling of the trade-off curve. We applied the algorithm on a synthetic example and compared our results with that of Spiky deconvolution approach.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar2008SINBADcrd/kumar2008SINBADcrd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/kumar08SINBADcrd/kumar08SINBADcrd_pres.pdf},
	author = {Vishal Kumar}
}
@conference {kumar2008CSEGcrs,
	title = {Curvelet-regularized seismic deconvolution},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2008},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SLIM},
	url = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs.pdf},
	presentation = {http://slim.gatech.edu/Publications/Public/Conferences/CSEG/2008/kumar2008CSEGcrs/kumar2008CSEGcrs_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {kumar2008SEGdwc,
	title = {Deconvolution with curvelet-domain sparsity},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {1996-2000},
	publisher = {SEG},
	organization = {SEG},
	abstract = {There is an inherent continuity along reflectors of a seismic image. We use the recently introduced multiscale and multidirectional curvelet transform to exploit this continuity along reflectors for cases in which the assumption of spiky reflectivity may not hold. We show that such type of seismic reflectivity can be represented in the curvelet-domain by a vector whose entries decay rapidly. This curvelet-domain compression of reflectivity opens new perspectives towards solving classical problems in seismic processing including the deconvolution problem. In this paper, we present a formulation that seeks curvelet-domain sparsity for non-spiky reflectivity and we compare our results with those of spiky deconvolution.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059287},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/kumar08SEGdwc/kumar08SEGdwc_pres.pdf},
	author = {Vishal Kumar and Felix J. Herrmann}
}
@conference {herrmann2005EAGEosf,
	title = {Optimization strategies for sparseness- and continuity-enhanced imaging: theory},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {Two complementary solution strategies to the least-squares migration problem with sparseness- and continuity constraints are proposed. The applied formalism explores the sparseness of curvelets on the reflectivity and their invariance under the demigration-migration operator. Sparseness is enhanced by (approximately) minimizing a (weighted) l1-norm on the curvelet coefficients. Continuity along imaged reflectors is brought out by minimizing the anisotropic diffusion or total variation norm which penalizes variations along and in between reflectors. A brief sketch of the theory is provided as well as a number of synthetic examples. Technical details on the implementation of the optimization strategies are deferred to an accompanying paper: implementation.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGEosf/Herrmann05EAGEosf.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1343},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and R. Kirlin}
}
