@unpublished {yin2024tfp,
	title = {Time-lapse full-waveform permeability inversion: a feasibility study},
	year = {2024},
	month = {3},
	abstract = {Time-lapse seismic monitoring necessitates integrated workflows that combine seismic and reservoir modeling to enhance reservoir property estimation. We present a feasibility study of an end-to-end inversion framework that directly inverts for permeability from prestack time-lapse seismic data. To assess the method{\textquoteright}s robustness, we design experiments focusing on its sensitivity to initial models and potential errors in modeling. Our study leverages the Compass model to simulate CO2 storage in saline aquifers, which is derived from well and seismic data from the North Sea, a candidate site for geological carbon storage.},
	keywords = {4D, CCS, coupled inversion, end-to-end, fluid-flow, GCS, inversion, monitoring, time-lapse},
	doi = {10.48550/arXiv.2403.04083},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2024/yin2024tfp/paper.html},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@conference {yin2023IMAGEcpi,
	title = {Coupled physics inversion for geological carbon storage monitoring},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Understanding CO2 plume behavior is key to the success of geological carbon storage projects. While two-phase flow equations provide a good model to make predictions on future CO2 plume behavior, these equations rely on having access to the true permeability model. Unfortunately, accurate information on the permeability is unavailable, which greatly jeopardizes our ability to predict CO2 plume behavior. To overcome this problem, we estimate the permeability from time-lapse seismic data via a coupled inversion methodology that improve as more monitoring data becomes available over time.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, monitoring, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/yin2023IMAGEend2end/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/yin2023IMAGEcpi},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICe2e,
	title = {End-to-end permeability inversion from prestack time-lapse seismic data: a case study on Compass model},
	year = {2023},
	month = {11},
	abstract = {Effective geological carbon storage hinges on a deep understanding of CO2 plume behavior. The dynamics of these plumes can be modeled using multiphase flow equations, but their accuracy is tied to a precise permeability model. A significant challenge is that we often lack detailed permeability data, limiting our predictive capabilities. To bridge this gap, we{\textquoteright}ve developed a multiphysics inversion method. This technique inverts for the permeability from observed time-lapse seismic data. Through a case study on the Compass model, we{\textquoteright}ve compared this approach with traditional 4D FWI in forecasting CO2 plume movements. Additionally, our research delves into how different initial permeability models, acquisition setups, and survey frequencies affect the results. Across the board, the inversion method not only enhances our current estimations but also provides valuable insights into future plume dynamics, even without continuous monitoring.},
	keywords = {CCS, coupled inversion, end-to-end, fluid-flow, inversion, ML4SEISMIC, monitoring, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICe2e},
	author = {Ziyi Yin and Mathias Louboutin and Olav M{\o}yner and Felix J. Herrmann}
}
@article {louboutin2023lmi,
	title = {Learned multiphysics inversion with differentiable programming and machine learning},
	journal = {The Leading Edge},
	volume = {42},
	year = {2023},
	note = {(The Leading Edge)},
	month = {07},
	pages = {452-516},
	abstract = {We present the Seismic Laboratory for Imaging and Modeling/Monitoring (SLIM) open-source software framework for computational geophysics and, more generally, inverse problems involving the wave-equation (e.g., seismic and medical ultrasound), regularization with learned priors, and learned neural surrogates for multiphase flow simulations. By integrating multiple layers of abstraction, our software is designed to be both readable and scalable. This allows researchers to easily formulate their problems in an abstract fashion while exploiting the latest developments in high-performance computing. We illustrate and demonstrate our design principles and their benefits by means of building a scalable prototype for permeability inversion from time-lapse crosswell seismic data, which aside from coupling of wave physics and multiphase flow, involves machine learning.},
	keywords = {CCS, deep learning, Fourier neural operators, GCS, Imaging, inversion, invertible networks, machine learning, monitoring, multiphysics, Normalizing flows, SLIM, software, time-lapse},
	url = {https://library.seg.org/doi/10.1190/tle42070474.1},
	presentation = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/TheLeadingEdge/2023/louboutin2023lmi/le_software.html},
	author = {Mathias Louboutin and Ziyi Yin and Rafael Orozco and Thomas J. Grady II and Ali Siahkoohi and Gabrio Rizzuti and Philipp A. Witte and Olav M{\o}yner and Gerard J. Gorman and Felix J. Herrmann}
}
@presentation {li2023ML4SEISMICmci,
	title = {Maximizing CO2 injectivity within fracture pressure},
	year = {2023},
	month = {11},
	abstract = {In geological carbon storage projects, optimizing CO2 injection strategies is paramount to enhance storage efficiency and prevent leakage. The objective is to maximize the CO2 injection volume without surpassing the fracture pressure. Traditional adjoint-based approaches necessitate extensive numerical simulations, leading to significant computational overhead. To circumvent this challenge, we introduce an optimization framework based on physics-informed deep convolutional neural networks. Trained on different permeability slices, our model can rapidly predict the maximal CO2 injection volume for new permeability fields in real-time.},
	keywords = {CCS, ML4SEISMIC, Optimization, reservoir simulation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/li2023ML4SEISMICmci},
	author = {Haoyun Li and Ziyi Yin and Olav M{\o}yner and Felix J. Herrmann}
}
@article {lopez2020gsb,
	title = {Spectral Gap-Based Seismic Survey Design},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	year = {2023},
	month = {1},
	abstract = {Seismic imaging in challenging sedimentary basins and reservoirs requires acquiring, processing, and imaging very large volumes of data (tens of terabytes). To reduce the cost of acquisition and the time from acquiring the data to producing a subsurface image, novel acquisition systems based on compressive sensing, low-rank matrix recovery, and randomized sampling have been developed and implemented. These approaches allow practitioners to achieve dense wavefield reconstruction from a substantially reduced number of field samples. However, designing acquisition surveys suited for this new sampling paradigm remains a critical and challenging role in oil, gas, and geothermal exploration. Typical random designs studied in the low-rank matrix recovery and compressive sensing literature are difficult to achieve by standard industry hardware. For practical purposes, a compromise between stochastic and realizable samples is needed. In this paper, we propose a deterministic and computationally cheap tool to alleviate randomized acquisition design, prior to survey deployment and large-scale optimization. We consider universal and deterministic matrix completion results in the context of seismology, where a bipartite graph representation of the source-receiver layout allows for the respective spectral gap to act as a quality metric for wavefield reconstruction. We provide realistic scenarios to demonstrate the utility of the spectral gap as a flexible tool that can be incorporated into existing survey design workflows for successful seismic data acquisition via low-rank and sparse signal recovery.},
	keywords = {biadjacency matrix, bipartite graph, Compressive Sensing, IEEE, low rank matrix completion, nuclear norm minimization, seismic data, seismic trace interpolation, spectral gap},
	doi = {10.1109/TGRS.2023.3237464},
	url = {https://slim.gatech.edu/Publications/Public/Journals/IEEETGRS/2023/lopez2020gsb/lopez2020gsb.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Nick Moldoveanu and Felix J. Herrmann}
}
@presentation {yin2022TRANSFORMjulia,
	title = {Julia for Geoscience},
	year = {2022},
	month = {04},
	abstract = {In this tutorial, we will introduce the Julia programming language to the geoscience community, covering topics such as I/O, data processing, inversion, and machine learning. We will begin by installing Julia and relevant packages. Through a series of tutorials, we will demonstrate Julia{\textquoteright}s abstraction power and show how to load and plot data, write your own functions/operators, form and solve a geophysical inverse problem, and demonstrate how to integrate wave-equation solvers in Julia with machine learning frameworks. The intent of this presentation is to provide an introductory level tutorial that will be useful to members of the geoscience community.},
	keywords = {JOLI, JUDI, Julia, machine learning, SLIM, software, TRANSFORM},
	url = {https://transform.softwareunderground.org/2022-julia-for-geoscience},
	url2 = {https://www.youtube.com/watch?v=HyWfp3NzIbg},
	software = {https://github.com/slimgroup/SLIMTutorials},
	author = {Ziyi Yin and Mathias Louboutin and Philipp A. Witte and Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Henryk Modzelewski and Felix J. Herrmann}
}
@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}
}
@article {sanavi2021tad,
	title = {True amplitude depth migration using curvelets},
	journal = {Geophysics},
	volume = {86},
	number = {4},
	year = {2021},
	note = {(Geophysics)},
	month = {07},
	pages = {S299-S310},
	abstract = {We have developed a true amplitude solution to the seismic imaging problem. We derive a diagonal scaling approach for the normal operator approximation in the curvelet domain. This is based on the theorem that states that curvelets remain approximately invariant under the action of the normal operator. We use curvelets as essential tools for approximation and inversion. We also exploit the theorem that states that the curvelet-domain approximation should be smooth in phase space by enforcing the smoothness of curvelet coefficients in the angle and space domains. We analyze our method using a reverse time migration-demigration code, simulating the acoustic wave equation on different synthetic models. Our method produces a good resolution with reflecting dips, reproduces the true amplitude reflectors, and compensates for incomplete illumination in seismic images.},
	keywords = {amplitude, depth migration, Imaging, inversion, LSRTM},
	doi = {10.1190/geo2019-0307.1},
	author = {Hamideh Sanavi and Peyman P. Moghaddam and Felix J. Herrmann}
}
@article {witte2019TPDedas,
	title = {An Event-Driven Approach to Serverless Seismic Imaging in the Cloud},
	journal = {IEEE Transactions on Parallel and Distributed Systems},
	volume = {31},
	number = {9},
	year = {2020},
	note = {(IEEE Transactions on Parallel and Distributed Systems)},
	month = {03},
	pages = {2032-2049},
	abstract = {Adapting the cloud for high-performance computing (HPC) is a challenging task, as software for HPC applications hinges on fast network connections and is sensitive to hardware failures. Using cloud infrastructure to recreate conventional HPC clusters is therefore in many cases an infeasible solution for migrating HPC applications to the cloud. As an alternative to the generic lift and shift approach, we consider the specific application of seismic imaging and demonstrate a serverless and event-driven pproach for running large-scale instances of this problem in the cloud. Instead of permanently running compute instances, our workflow is based on a serverless architecture with high throughput batch computing and event-driven computations, in which computational resources are only running as long as they are utilized. We demonstrate that this approach is very flexible and allows for resilient and nested levels of parallelization, including domain decomposition for solving the underlying partial differential equations. While the event-driven approach introduces some overhead as computational resources are repeatedly restarted, it inherently provides resilience to instance shut-downs and allows a significant reduction of cost by avoiding idle instances, thus making the cloud a viable alternative to on-premise clusters for large-scale seismic imaging.},
	keywords = {cloud, event-driven, Imaging, LSRTM, serverless},
	doi = {10.1109/TPDS.2020.2982626},
	url = {https://slim.gatech.edu/Publications/Public/Journals/IEEETPDS/2020/witte2019TPDedas/witte2019TPDedas.html},
	author = {Philipp A. Witte and Mathias Louboutin and Henryk Modzelewski and Charles Jones and James Selvage and Felix J. Herrmann}
}
@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 {witte2019SEGedw,
	title = {Event-driven workflows for large-scale seismic imaging in the cloud},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {3984-3988},
	abstract = {Cloud computing has seen a large rise in popularity in recent years and is becoming a cost effective alternative to on-premise computing, with theoretically unlimited scalability. However, so far little progress has been made in adapting the cloud for high performance computing (HPC) tasks, such as seismic imaging and inversion. As the cloud does not provide the same type of fast and reliable connections as conventional HPC clusters, porting legacy codes developed for HPC environments to the cloud is ineffective and misses out on an opportunity to take advantage of new technologies presented by the cloud. We present a novel approach of bringing seismic imaging and inversion workflows to the cloud, which does not rely on a traditional HPC environment, but is based on serverless and event-driven computations. Computational resources are assigned dynamically in response to events, thus minimizing idle time and providing resilience to hardware failures. We test our workflow on two large-scale imaging examples and demonstrate that cost-effective HPC in the cloud is possible, but requires careful reconsiderations of how to bring software to the cloud.},
	keywords = {cloud, Imaging, large-scale, LS-RTM, RTM, SEG, workflow},
	doi = {10.1190/segam2019-3215069.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw_pres.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Henryk Modzelewski and Charles Jones and James Selvage 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 {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 {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}
}
@presentation {kumar2017SINBADFfas,
	title = {Full-azimuth seismic data processing w/ coil acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we will demonstrate the performance of our in-house 5D low-rank based interpolation method on a seismic data acquired using coil shooting full-azimuth acquisition. We will show that we can recover full-azimuthal interpolated data from highly subsampled data, where the subsampling ratio is 4\%. This is the first time, we are testing our interpolation ideas on real 3D marine seismic data acquisition. Our findings show that we can avoid the general practice of windowing the data while performing the interpolation, specially using rank-minimization based framework.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/kumar2017SINBADFfas/kumar2017SINBADFfas.mov},
	author = {Rajiv Kumar and Nick Moldoveanu and Keegan Lensink and Felix J. Herrmann}
}
@presentation {lopez2017SINBADFmci,
	title = {Matrix Completion in Parallel Architectures: Julia Implementation},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Matrix completion techniques offer potential tools for frugal seismic data acquisition, where dense acquisition is replaced by optimization. This shift of focus means that efficient numerical methods are critical for the implementation of these techniques in large-scale seismic applications. To this end, this talk modifies rank-penalization methodologies to suit parallel architectures. By adopting factorization-based alternating minimization schemes, each program can be decoupled into independent sub-problems handled in parallel. We showcase a distributed parallel execution in Julia and explore the scalability of the approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/lopez2017SINBADFmci/lopez2017SINBADFmci.mov},
	author = {Oscar Lopez and Keegan Lensink and Rajiv Kumar and Henryk Modzelewski}
}
@article {kumar2014GEOPemc,
	title = {Efficient matrix completion for seismic data reconstruction},
	journal = {Geophysics},
	volume = {80},
	number = {5},
	year = {2015},
	note = {(Geophysics)},
	month = {09},
	pages = {V97-V114},
	abstract = {Despite recent developments in improved acquisition, seismic data often remain undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We have interpreted the missing-trace interpolation problem in the context of matrix completion (MC), and developed three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We use an optimization program that restores this low-rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we found experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by using the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, to work with computationally feasible algorithms for large-scale data, we use a factorization-based approach to MC, which significantly speeds up the computations compared with repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also find that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carried out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic 2D and 3D seismic data sets show that our method compares favorably in terms of computational speed and recovery quality with existing curvelet- and tensor-based techniques.},
	keywords = {2D, 3D, algorithm, interpolation, low-rank, signal processing},
	doi = {10.1190/geo2014-0369.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2015/kumar2014GEOPemc/kumar2014GEOPemc.pdf},
	url2 = {http://library.seg.org/doi/abs/10.1190/geo2014-0369.1},
	author = {Rajiv Kumar and Curt Da Silva and Okan Akalin and Aleksandr Y. Aravkin and Hassan Mansour and Ben Recht 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}
}
@mastersthesis {miao2014THesi,
	title = {Efficient seismic imaging with spectral projector and joint sparsity},
	year = {2014},
	note = {(MSc)},
	month = {03},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {In this thesis, we investigate the potential of improving the eciency of seismic imaging with two advanced techniques: the spectral projector and the {\textquoteright}joint sparsity{\textquoteright}. The spectral projector offers an eigenvalue decomposition free computation routine that can filter out unstable evanescent wave com- ponents during wave equation based depth extrapolation. {\textquoteright}Joint sparsity{\textquoteright} aims to improve on the pure sparsity promoting recovery by making use of additional structure information of the signal. Besides, a new sparsity optimization algorithm - PQNL1 - is proposed to improve both theoretical convergence rate and practical performance for extremely large seismic imaging problems.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi_pres.pdf},
	author = {Lina Miao}
}
@article {aravkin2014SISCfmd,
	title = {Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation},
	journal = {SIAM Journal on Scientific Computing},
	volume = {36},
	number = {5},
	year = {2014},
	note = {(SISC)},
	month = {10},
	pages = {S237-S266},
	abstract = {Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic data interpolation. In this paper, we consider matrix completion formulations designed to hit a target data-fitting error level provided by the user, and we propose an algorithm called LR-BPDN that is able to exploit factorized formulations to solve the corresponding optimization problem. Since practitioners typically have strong prior knowledge about target error level, this innovation makes it easy to apply the algorithm in practice, leaving only the factor rank to be determined. Within the established framework, we propose two extensions that are highly relevant to solving practical challenges of data interpolation. First, we propose a weighted extension that allows known subspace information to improve the results of matrix completion formulations. We show how this weighting can be used in the context of frequency continuation, an essential aspect to seismic data interpolation. Second, we propose matrix completion formulations that are robust to large measurement errors in the available data. We illustrate the advantages of LR-BPDN on collaborative filtering problems using the MovieLens 1M and 10M and Netflix 100M datasets. Then we use the new method, along with its robust and subspace reweighted extensions, to obtain high-quality reconstructions for large-scale seismic interpolation problems with real data, even in the presence of data contamination.},
	keywords = {factorized formulations, matrix completion, robust statistics, seismic trace interpolation, variational optimization},
	doi = {10.1137/130919210},
	url = {https://slim.gatech.edu/Publications/Public/Journals/SIAMJournalOnScientificComputing/2014/aravkin2014SISCfmd/aravkin2014SISCfmd.pdf},
	url2 = {http://epubs.siam.org/doi/abs/10.1137/130919210},
	author = {Aleksandr Y. Aravkin and Rajiv Kumar and Hassan Mansour and Ben Recht and Felix J. Herrmann}
}
@article {ghadermarzy2013ncs,
	title = {Non-convex compressed sensing using partial support information},
	journal = {Journal of Sampling Theory in Signal and Image Processing},
	volume = {13},
	number = {3},
	year = {2014},
	pages = {249-270},
	abstract = {In this paper we address the recovery conditions of weighted $\ell_p$ minimization for signal reconstruction from compressed sensing measurements when partial support in- formation is available. We show that weighted $\ell_p$ minimization with 0 < p < 1 is stable and robust under weaker sufficient conditions compared to weighted $\ell_1$ minimization. Moreover, the sufficient recovery conditions of weighted $\ell_p$ are weaker than those of regular $\ell_p$ minimization if at least 50\% of the support estimate is accurate. We also review some algorithms which exist to solve the non-convex $\ell_p$ problem and illustrate our results with numerical experiments.},
	keywords = {compressed sensing, nonconvex optimization, sparse reconstruction, weighted $\ell_p$},
	url = {http://arxiv.org/abs/1311.3773},
	author = {Navid Ghadermarzy and Hassan Mansour and Ozgur Yilmaz}
}
@conference {miao2014SEGrhss,
	title = {Randomized HSS acceleration for full-wave-equation depth stepping migration},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {3752-3756},
	abstract = {In this work we propose to use the spectral projector (Kenney and Laub, 1995) and randomized HSS technique (Chandrasekaran et al., 2006) to achieve a stable and affordable two-way wave equation depth stepping migration algorithm.},
	keywords = {acoustic, depth extrapolation, full wave equation migration, randomized SVD, SEG, spectral projector},
	doi = {http://dx.doi.org/10.1190/segam2014-1417.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/miao2014SEGrhss/miao2014SEGrhss.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/miao2014SEGrhss/miao2014SEGrhss_pres.pdf},
	author = {Lina Miao and Polina Zheglova 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 {miao2013CSEGaospsa,
	title = {Acceleration on sparse promoting seismic applications},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2013},
	month = {05},
	abstract = {Sparse promoting oriented problems are never new in seismic applications. Back in 1970s, geophysicists had well exploited the robustness of sparse solutions. Moreover, with the emerging usage of compressed sensing in recent years, sparse recovery have been favored in dealing with {\textquoteright}curse of dimensionality{\textquoteright} in various seismic field acquisition, data processing, and imaging applications. Although sparsity has provided a promising approach, solving for it presents a big challenge. How to work efficiently with the extremely large-scale seismic problem, and how to improve the convergence rate reducing computation time are most frequently asked questions in this content. In this abstract, the author proposed a new algorithm {\textendash} PQN$\ell_1$, trying to address those questions. One example on seismic data processing is included.},
	keywords = {CSEG, Projected Quasi Newton, sparsity-promotion, SPG$\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/miao2013CSEGaospsa/miao2013CSEGaospsa.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@article {shahidi2013GPars,
	title = {Application of randomized sampling schemes to curvelet-based sparsity-promoting seismic data recovery},
	journal = {Geophysical Prospecting},
	volume = {61},
	number = {5},
	year = {2013},
	month = {09},
	pages = {973-997},
	abstract = {Reconstruction of seismic data is routinely used to improve the quality and resolution of seismic data from incomplete acquired seismic recordings. Curvelet-based Recovery by Sparsity-promoting Inversion, adapted from the recently-developed theory of compressive sensing, is one such kind of reconstruction, especially good for recovery of undersampled seismic data. Like traditional Fourier-based methods, it performs best when used in conjunction with randomized subsampling, which converts aliases from the usual regular periodic subsampling into easy-to-eliminate noise. By virtue of its ability to control gap size, along with the random and irregular nature of its sampling pattern, jittered (sub)sampling is one proven method that has been used successfully for the determination of geophone positions along a seismic line. In this paper, we extend jittered sampling to two-dimensional acquisition design, a more difficult problem, with both underlying Cartesian, and hexagonal grids. We also study what we term separable and non-separable two-dimensional jittered samplings. We find hexagonal jittered sampling performs better than Cartesian jittered sampling, while fully non-separable jittered sampling performs better than separable jittered sampling. Two other 2D randomized sampling methods, Poisson Disk sampling and Farthest Point sampling, both known to possess blue-noise spectra, are also shown to perform well.},
	keywords = {curvelets, Geophysical Prospecting, randomized sampling},
	doi = {10.1111/1365-2478.12050},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2013/shahidi2013GPars/shahidi2013GPars.pdf},
	author = {Reza Shahidi and Gang Tang and Jianwei Ma and Felix J. Herrmann}
}
@presentation {macedo2013SINBADdap,
	title = {A dual approach to PhaseLift via gauge programming and bundle methods},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A feature common to many sparse optimization problems is that the number of variables may be significantly larger than the number of constraints- e.g., the matrix-lifting approach taken by PhaseLift for phase retrieval results in a problem where the number of variables is quadratic in the number of constraints. We consider a duality framework and numerical methods to leverage the relatively small number of constraints. Preliminary numerical results illustrate our approach and its flexibility.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
	author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {miao2013SINBADfid,
	title = {Fast imaging via depth stepping with the two-way wave equation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this presentation we propose a fast imaging algorithm via depth stepping with the two-way wave equation. Within the framework of survey sinking, a stabilized depth extrapolation operator is computed using a spectral projector which can efficiently split evanescent wave components. The computation of the spectral projector features with an Hierarchically Semi-Seperable (HSS) matrix representation speeded up polynomial recursion, resulting in an accleration from cubic numerical complexity to linear numerical complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/miao2013SINBADfid/miao2013SINBADfid.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@article {mansour2013GEOPiwr,
	title = {Improved wavefield reconstruction from randomized sampling via weighted one-norm minimization},
	journal = {Geophysics},
	volume = {78},
	number = {5},
	year = {2013},
	month = {08},
	pages = {V193-V206},
	abstract = {Missing-trace interpolation aims to recover the gaps caused by physical obstacles or deliberate subsampling to control acquisition costs in otherwise regularly sampled seismic wavefields. Although transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, current recovery techniques do not fully utilize a priori information derived from the locations of the transform-domain coefficients, especially when curvelet domain sparsity is exploited. We use recovery by weighted one-norm minimization, which exploits correlations between the locations of significant curvelet coefficients of different partitions, e.g., shot records, common-offset gathers, or frequency slices of the acquired data. We use these correlations to define a sequence of 2D curvelet-based recovery problems that exploit 3D continuity exhibited by seismic wavefields without relying on the highly redundant 3D curvelet transform. To test the performance of our weighted algorithm, we compared recoveries from different data sorting and partitioning scenarios for a seismic line from the Gulf of Suez. These tests demonstrated that our method is superior to standard $\ell_1$ minimization in terms of antialiasing capability, reconstruction quality and computational memory requirements.},
	keywords = {compressed sensing, randomized sampling, Trace interpolation, weighted one-norm minimization},
	doi = {10.1190/geo2012-0383.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2013/mansour2013GEOPiwr/mansour2013GEOPiwr.pdf},
	author = {Hassan Mansour and Felix J. Herrmann and Ozgur Yilmaz}
}
@article {moghaddam2013GEOPnoa,
	title = {A new optimization approach for source-encoding full-waveform inversion},
	journal = {Geophysics},
	volume = {78},
	number = {3},
	year = {2013},
	month = {05},
	pages = {R125-R132},
	abstract = {Waveform inversion is the method of choice for determining highly heterogeneous subsurface structure. However, conventional waveform inversion requires that the wavefield for each source is computed separately. This makes it very expensive for realistic 3D seismic surveys. Source-encoding waveform inversion, in which the sources are modelled simultaneously, is considerably faster than conventional waveform inversion but suffers from artifacts. These artifacts can partly be removed by assigning random weights to the source wavefields. We found that the misfit function, and therefore also its gradient, for source-encoding waveform inversion is an unbiased random estimation of the misfit function used in conventional waveform inversion. We found a new method of source-encoding waveform inversion which takes into account the random nature of the gradients used in the optimization. In this new method, the gradient at each iteration is a weighted average of past gradients such that the most recent gradients have the largest weights with exponential decay. This way we damped the random fluctuations of the gradient by incorporating information from the previous iterations. We compare this new method with existing source-encoding waveform inversion methods as well as conventional waveform inversion and found that the model misfit reduction is faster and smoother than those of existing source-encoding waveform inversion methods, and it approaches the model misfit reduction obtained in conventional waveform inversion.},
	keywords = {FWI, Geophysics, Optimization, source encoding},
	doi = {10.1190/GEO2012-0090.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2013/moghaddam2013GEOPnoa/moghaddam2013GEOPnoa.pdf},
	author = {Peyman P. Moghaddam and Henk Keers and Felix J. Herrmann and Wim A. Mulder}
}
@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}
}
@presentation {kumar2013SINBADhss,
	title = {Seismic data interpolation via low-rank matrix factorization in the hierarchical semi-separable representation},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. One of the main requirements of exploiting rank-minimization approaches is that the target data set should exhibit a low-rank structure. Seismic frequency slices exhibit low-rank structure at the low-frequencies, but not at the high frequencies. This behavior is due to the increase in oscillations as we move from low to high-frequency slices, even though the energy remains focused around the diagonal. Therefore, interpolation via rank minimization in the high-frequency range requires extended formulations that incorporate low-rank structure. 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 = {Presentation, SINBAD, SINBADSPRING2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Spring/kumar2013SINBADhss/kumar2013SINBADhss.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@techreport {kumar2013ICMLlr,
	title = {An SVD-free Pareto curve approach to rank minimization},
	number = {TR-EOAS-2013-2},
	year = {2013},
	month = {02},
	institution = {UBC},
	abstract = {Recent SVD-free matrix factorization formulations have enabled rank optimization for extremely large-scale systems (millions of rows and columns). In this paper, we consider rank-regularized formulations that only require a target data-fitting error level, and propose an algorithm for the corresponding problem. We illustrate the advantages of the new approach using the Netflix problem, and use it to obtain high quality results for seismic trace interpolation, a key application in exploration geophysics. We show that factor rank can be easily adjusted as the inversion proceeds, and propose a weighted extension that allows known subspace information to improve the results of matrix completion formulations. Using these methods, we obtain high-quality reconstructions for large scale seismic interpolation problems with real data.},
	keywords = {interpolation, low rank},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/kumar2013ICMLlr/kumar2013ICMLlr.pdf},
	author = {Aleksandr Y. Aravkin and Rajiv Kumar and Hassan Mansour and Ben Recht and Felix J. Herrmann}
}
@presentation {fang2013SINBADsfwi,
	title = {Swift FWI},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In 3D case, there is much more data and modeling is much more expensive. As a result, parallel computing is very important for 3D full-waveform inversion. Both domain decomposition and data decomposition need a large number of parallel computing resources. However, programs based on parallel MATLAB suffer from the limitation of licenses. In order to obtain a MATLAB licenses free solution, we use SWIFT, which is a fast and easy parallel scripting language. Once the original Mat file is complied to an executable file, SWIFT can run the code inside the executable file in parallel without using parallel MATLAB. We use the SWIFT to compute the object functions and gradients of different shots in parallel, and test the parallel 3D FWI code with overthrust data. This is joint work with Thomas Lai, Harsh Juneja, Bas Peters.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADsfwi/fang2013SINBADsfwi.pdf},
	author = {Zhilong Fang and Henryk Modzelewski and Tristan van Leeuwen and Thomas Lai and Harsh Juneja and Bas Peters and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
@presentation {miao2012SINBADasp,
	title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Sparse promoting recovery problem arises more and more frequently with the broad application of compressed sensing tool in exploration seismology. Because of the curse of dimensionality, the prohibitive computation burden on iteratively evaluating objective functions is one of the key issues that constrain high performance l1 solver. In this paper, we try to further improve the convergence performance of SPGl1, one of the state-of-the-art large scale sparse recovery solver, and as a result limit the number of objective function evaluations by introducing a projected quasi Newton method. Examples showing acceleration on seismic data collection, data processing as well as inversion are included.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
	author = {Lina Miao}
}
@conference {mansour2012ICASSadapt,
	title = {Adaptive compressed sensing for video acquisition},
	booktitle = {ICASSP},
	year = {2012},
	abstract = {In this paper, we propose an adaptive compressed sensing scheme that utilizes a support estimate to focus the measurements on the large valued coefficients of a compressible signal. We embed a "sparse-filtering" stage into the measurement matrix by weighting down the contribution of signal coefficients that are outside the support estimate. We present an application which can benefit from the proposed sampling scheme, namely, video compressive acquisition. We demonstrate that our proposed adaptive CS scheme results in a significant improvement in reconstruction quality compared with standard CS as well as adaptive recovery using weighted $\ell_1$ minimization.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/MansourYilmazICASSPaCS/MansourYilmazICASSPaCS.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@conference {mansour2012SSPwspgl1,
	title = {Beyond $\ell_1$ norm minimization for sparse signal recovery},
	booktitle = {2012 IEEE Statistical Signal Processing Workshop (SSP) (SSP{\textquoteright}12)},
	year = {2012},
	month = {03},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Ann Arbor, Michigan, USA},
	abstract = {Sparse signal recovery has been dominated by the basis pursuit denoise (BPDN) problem formulation for over a decade. In this paper, we propose an algorithm that outperforms BPDN in finding sparse solutions to underdetermined linear systems of equations at no additional computational cost. Our algorithm, called WSPGL1, is a modification of the spectral projected gradient for $\ell_1$ minimization (SPGL1) algorithm in which the sequence of LASSO subproblems are replaced by a sequence of weighted LASSO subproblems with constant weights applied to a support estimate. The support estimate is derived from the data and is updated at every iteration. The algorithm also modifies the Pareto curve at every iteration to reflect the new weighted $\ell_1$ minimization problem that is being solved. We demonstrate through extensive simulations that the sparse recovery performance of our algorithm is superior to that of $\ell_1$ minimization and approaches the recovery performance of iterative re-weighted $\ell_1$ (IRWL1) minimization of Cand{\`e}s, Wakin, and Boyd. Moreover, our algorithm has the computational cost of a single BPDN problem.},
	keywords = {compressed sensing, iterative algorithms, partial support recovery, Sparse recovery, weighted $\ell_1$ minimization},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SSP/2012/mansour2012SSPwspgl1/mansour2012SSPwspgl1.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SSP/2012/mansour2012SSPwspgl1/mansour2012SSPwspgl1_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {au-yeung2012SINBADcs,
	title = {Compressed sensing, random Fourier matrix and jitter sampling},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Compressed sensing is an emerging signal processing technique that allows signals to be sampled well below the Nyquist rate, when the signal has a sparse representation in an orthonormal basis. By using a random Fourier matrix or a Gaussian matrix as our measurement matrix, we can reconstruct a signal from far fewer measurements than required by Shannon sampling theorem. In this talk, we will discuss the role of uniform versus jitter sampling, both in a theoretical and practical viewpoint.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/au-yeung2012SINBADcs/au-yeung2012SINBADcs_pres.pdf},
	author = {Enrico Au-Yeung and Hassan Mansour and Ozgur Yilmaz}
}
@conference {min2012EAGEefwi,
	title = {Frequency-domain elastic waveform inversion using weighting factors related to source-deconvolved residuals},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {One of the limitations in seismic waveform inversion is that inversion results are very sensitive to initial guesses, which may be because the gradients computed at each frequency are not properly weighted depending on given models.Analyzingthe conventional waveform inversion algorithms using the pseudo-Hessian matrix as a pre-conditioner shows that the gradientsdo not properly describe the feature of given models or high- and low-end frequencies do not contribute the model parameter updates due to banded spectra of source wavelet. For a better waveform inversion algorithm, we propose applying weighting factors to gradients computed at each frequency. The weighting factors are designed using the source-deconvolved back-propagated wavefields. Numerical results for the SEG/EAGE salt model show that the weighting method improves gradient images and its inversion results are compatible with true velocities even with poorly estimated initial guesses.},
	keywords = {EAGE, elastic, frequency-domain, waveform inversion, weighting factors},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/min2012EAGEefwi/min2012EAGEefwi.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59623},
	author = {Ju-Won Oh and Dong-Joo Min and Felix J. Herrmann}
}
@article {Mansour11TRssma,
	title = {Randomized marine acquisition with compressive sampling matrices},
	journal = {Geophysical Prospecting},
	volume = {60},
	number = {4},
	year = {2012},
	month = {07},
	pages = {648-662},
	abstract = {Seismic data acquisition in marine environments is a costly process that calls for the adoption of simultaneous-source or randomized acquisition - an emerging technology that is stimulating both geophysical research and commercial efforts. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. In this paper, we discuss the properties of a specific class of randomized simultaneous acquisition matrices and demonstrate that sparsity-promoting recovery improves the quality of reconstructed seismic data volumes. We propose a practical randomized marine acquisition scheme where the sequential sources fire airguns at only randomly time-dithered instances. We demonstrate that the recovery using sparse approximation from random time-dithering with a single source approaches the recovery from simultaneous-source acquisition with multiple sources. Established findings from the field of compressive sensing indicate that the choice of the sparsifying transform that is incoherent with the compressive sampling matrix can significantly impact the reconstruction quality. Leveraging these findings, we then demonstrate that the compressive sampling matrix resulting from our proposed sampling scheme is incoherent with the curvelet transform. The combined measurement matrix exhibits better isometry properties than other transform bases such as a non-localized multidimensional Fourier transform. We illustrate our results with simulations of "ideal" simultaneous-source marine acquisition, which dithers both in time and space, compared with periodic and randomized time-dithering.},
	keywords = {curvelet transform, Fourier, Marine acquisition},
	doi = {10.1111/j.1365-2478.2012.01075.x},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2478.2012.01075.x/abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2012/Mansour11TRssma/Mansour11TRssma.pdf},
	author = {Hassan Mansour and Haneet Wason and Tim T.Y. Lin and Felix J. Herrmann}
}
@article {mansour2012IEEETITrcs,
	title = {Recovering compressively sampled signals using partial support information},
	journal = {IEEE Transactions on Information Theory},
	volume = {58},
	number = {2},
	year = {2012},
	month = {02},
	pages = {1122-1134},
	publisher = {Department of Computer Science},
	address = {University of British Columbia, Vancouver},
	abstract = {We study recovery conditions of weighted $\ell_1$ minimization for signal reconstruction from compressed sensing measurements when partial support information is available. We show that if at least 50\% of the (partial) support information is accurate, then weighted $\ell_1$ minimization is stable and robust under weaker sufficient conditions than the analogous conditions for standard $\ell_1$ minimization. Moreover, weighted $\ell_1$ minimization provides better upper bounds on the reconstruction error in terms of the measurement noise and the compressibility of the signal to be recovered. We illustrate our results with extensive numerical experiments on synthetic data and real audio and video signals.},
	keywords = {Compressive Sensing},
	doi = {10.1109/TIT.2011.2167214},
	url = {https://slim.gatech.edu/Publications/Public/Journals/IEEETransInformationTheory/2012/mansour2012IEEETITrcs/mansour2012IEEETITrcs.pdf},
	author = {Michael P. Friedlander and Hassan Mansour and Rayan Saab and Ozgur Yilmaz}
}
@conference {min2012CSEGrgfe,
	title = {Re-establishment of gradient in frequency-domain elastic waveform inversion},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {To obtain solutions close to global minimum in waveform inversion, the gradients computed at each frequency need to be weighted to appropriately describe the residuals between modeled and field data. While the low-frequency components of the gradients should be weighted to recover the long-wavelength structures, the high-frequency components of the gradients need to be weighted when the short-wavelength structures are restored. However, the conventional elastic waveform inversion algorithms cannot properly weight the gradients computed at each frequency. When gradients are scaled using the pseudo-Hessian matrix inside the frequency loop, gradients obtained at high frequencies are over-emphasized. When the gradients are scaled outside the frequency loop, gradients are weighted by the source spectra. In this study, we propose applying weighting factors to the gradients obtained at each frequency so that gradients can properly reflect the differences between the true and assumed models satisfying the general inverse theory. The weighting factors are composed by the backpropagated residuals. Numerical examples for the simple rectangular-shaped model and the modified version of the Marmousi-2 model show that the weighting method enhances gradient images and inversion results compared to the conventional inversion algorithms.},
	keywords = {elastic, frequency-domain, waveform inversion, weighting factors},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/min2012CSEGrgfe/min2012CSEGrgfe.pdf},
	author = {Ju-Won Oh and Dong-Joo Min and Felix J. Herrmann}
}
@presentation {mansour2012SINBADsti,
	title = {Seismic trace interpolation via sparsity promoting reweighted algorithms},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Missing-trace interpolation aims to reconstruct regularly sampled wavefields from periodically sampled data with gaps caused by physical constraints. While transform-domain sparsity promotion has proven to be an effective tool to solve this recovery problem, current recovery techniques make no use of a priori information on the transform-domain coefficients. To overcome these vulnerabilities in solving the recovery problem for large-scale problems, we propose recovery by weighted one-norm minimization, which exploits correlations between locations of significant coefficients of different partitions, e.g., shot records, common-offset gathers, or frequency slices, of the acquired data. Moreover, in situations where no prior support estimate is available, we propose the WSPGL1 algorithm that outperforms standard $\ell_1$ minimization in finding sparse solutions to underdetermined linear systems of equations. Our algorithm is a modification of the SPGL1 algorithm and enjoys better sparse recovery performance at no additional computational cost. We illustrate the improved recovery using WSPGL1 for randomly subsampled seismic traces.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/mansour2012SINBADsti/mansour2012SINBADsti_pres.pdf},
	author = {Hassan Mansour}
}
@conference {mansour2012ICASSsupport,
	title = {Support driven reweighted $\ell_1$ minimization},
	booktitle = {ICASSP},
	year = {2012},
	abstract = {In this paper, we propose a support driven reweighted $\ell_1$ minimization algorithm (SDRL1) that solves a sequence of weighted $\ell_1$ problems and relies on the support estimate accuracy. Our SDRL1 algorithm is related to the IRL1 algorithm proposed by Candes, Wakin, and Boyd. We demonstrate that it is sufficient to find support estimates with good accuracy and apply constant weights instead of using the inverse coefficient magnitudes to achieve gains similar to those of IRL1. We then prove that given a support estimate with sufficient accuracy, if the signal decays according to a specific rate, the solution to the weighted $\ell_1$ minimization problem results in a support estimate with higher accuracy than the initial estimate. We also show that under certain conditions, it is possible to achieve higher estimate accuracy when the intersection of support estimates is considered. We demonstrate the performance of SDRL1 through numerical simulations and compare it with that of IRL1 and standard $\ell_1$ minimization.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/MansourYilmazICASSPwL1/MansourYilmazICASSPwL1.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@conference {mansour2011SBGFcspsma,
	title = {A compressive sensing perspective on simultaneous marine acquisition},
	booktitle = {SBGF},
	year = {2011},
	publisher = {SBGF},
	organization = {SBGF},
	abstract = {The high cost of acquiring seismic data in marine environments compels the adoption of simultaneous- source acquisition - an emerging technology that is stimulating both geophysical research and commercial efforts. In this paper, we discuss the properties of randomized simultaneous acquisition matrices and demonstrate that sparsity-promoting recovery improves the quality of the reconstructed seismic data volumes. Simultaneous marine acquisition calls for the development of a new set of design principles and post-processing tools. Leveraging established findings from the field of compressed sensing, the recovery from simultaneous sources depends on a sparsifying transform that compresses seismic data, is fast, and reasonably incoherent with the compressive sampling matrix. To achieve this incoherence, we use random time dithering where sequential acquisition with a single airgun is replaced by continuous acquisition with multiple airguns firing at random times and at random locations. We demonstrate our results with simulations of simultaneous Marine acquisition using periodic and randomized time dithering.},
	keywords = {Acquisition, Compressive Sensing, Presentation, SBGF},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SBGF/2011/Mansour11SBGFcspsma/Mansour11SBGFcspsma.pdf},
	author = {Hassan Mansour and Haneet Wason and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {vanleeuwen2011SIAMGEOmawt,
	title = {Multiscale aspects of waveform tomography},
	booktitle = {SIAMGEO},
	year = {2011},
	publisher = {SIAM GeoSciences 2011},
	organization = {SIAM GeoSciences 2011},
	abstract = {We consider the inference of medium velocity from transmitted acoustic waves. Typically, the measurements are done in a narrow frequency band. As a result the sensitivity of the data with respect to velocity perturbations varies dramatically with the scale of the perturbation. {\textquoteleft}Smooth{\textquoteright} perturbations will cause a phase shift, whereas perturbations that vary on the wavelength-scale cause amplitude variations. We investigate how to incorporate this scale dependent behavior in the formulation of the inverse problem.},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2011/vanleeuwen2011SIAMGEOmawt/vanleeuwen2011SIAMGEOmawt.pdf},
	author = {Tristan van Leeuwen and Wim A. Mulder}
}
@presentation {min2011SINBADpss,
	title = {Parameter-selection strategy for density in frequency-domain elastic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/min2011SINBADpss/min2011SINBADpss_pres.pdf},
	author = {Dong-Joo Min}
}
@conference {Mansour11TRwmmw,
	title = {Weighted -$\ell_1$ minimization with multiple weighting sets},
	booktitle = {Proc. SPIE},
	volume = {8138},
	year = {2011},
	month = {09},
	pages = {813809-813809-13},
	abstract = {In this paper, we study the support recovery conditions of weighted -$\ell_1$ minimization for signal reconstruction from compressed sensing measurements when multiple support estimate sets with different accuracy are available. We identify a class of signals for which the recovered vector from -$\ell_1$ minimization provides an accurate support estimate. We then derive stability and robustness guarantees for the weighted -$\ell_1$ minimization problem with more than one support estimate. We show that applying a smaller weight to support estimate that enjoy higher accuracy improves the recovery conditions compared with the case of a single support estimate and the case with standard, i.e., non-weighted,-$\ell_1$ minimization. Our theoretical results are supported by numerical simulations on synthetic signals and real audio signals.},
	keywords = {Compressive Sensing, Optimization},
	doi = {10.1117/12.894165},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2011/Mansour11TRwmmw/Mansour11TRwmmw.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@presentation {mansour2011SINBADwcw,
	title = {Why do curvelets work?},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/mansour2011SINBADwcw/mansour2011SINBADwcw_pres.pdf},
	author = {Hassan Mansour}
}
@mastersthesis {moghaddam10phd,
	title = {Curvelet-based migration amplitude recovery},
	year = {2010},
	note = {(PhD)},
	month = {05},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {Migration can accurately locate reflectors in the earth but in most cases fails to correctly resolve their amplitude. This might lead to mis-interpretation of the nature of reflector. In this thesis, I introduced a method to accurately recover the amplitude of the seismic reflector. This method relies on a new transform-based recovery that exploits the expression of seismic images by the recently developed curvelet transform. The elements of this transform, called curvelets, are multi-dimensional, multi-scale, and multi-directional. They also remain approximately invariant under the imaging operator. I exploit these properties of the curvelets to introduce a method called Curvelet Match Filtering (CMF) for recovering the seismic amplitude in presence of noise in both migrated image and data. I detail the method and illustrate its performance on synthetic dataset. I also extend CMF formulation to other geophysical applications and present results on multiple removal. In addition of that, I investigate preconditioning of the migration which results to rapid convergence rate of the iterative method using migration.},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2010/moghaddam10phd.pdf},
	author = {Peyman P. Moghaddam}
}
@presentation {moghaddam2010SINBADrfwi,
	title = {Randomized full-waveform inversion},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/moghaddam2010SINBADrfwi/moghaddam2010SINBADrfwi_pres.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {moghaddam2010SEGrfw,
	title = {Randomized full-waveform inversion: a dimenstionality-reduction approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {29},
	year = {2010},
	month = {10},
	pages = {977-982},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Full-waveform inversion relies on the collection of large multi-experiment data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While improvements in acquisition and inversion have been extremely successful, the current trend of incessantly pushing for higher quality models in increasingly complicated regions of the Earth reveals fundamental shortcomings in our ability to handle increasing problem sizes numerically. Two main culprits can be identified. First, there is the so-called {\textquoteleft}{\textquoteleft}curse of dimensionality{\textquoteright}{\textquoteright} exemplified by Nyquist{\textquoteright}s sampling criterion, which puts disproportionate strain on current acquisition and processing systems as the size and desired resolution increases. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to develop algorithms that are amenable to parallelization. In this paper, we discuss different strategies that address these issues via randomized dimensionality reduction.},
	keywords = {Full-waveform inversion, Optimization, Presentation, SEG},
	doi = {10.1190/1.3513940},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/moghaddam10SEGrfw/moghaddam10SEGrfw.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2010/moghaddam10SEGrfw/moghaddam10SEGrfw_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann}
}
@presentation {mansour2010SINBADrcs,
	title = {Recovering compressively sampled signals using partial support information},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/mansour2010SINBADrcs/mansour2010SINBADrcs_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {modzelewski2010SINBADsra,
	title = {Software releases and architecture},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/modzelewski2010SINBADsra/modzelewski2010SINBADsra_pres.pdf},
	author = {Henryk Modzelewski}
}
@article {herrmann2009GEOPcbm,
	title = {Curvelet-based migration preconditioning and scaling},
	journal = {Geophysics},
	volume = {74},
	year = {2009},
	month = {07-08},
	pages = {A41-A46},
	abstract = {The extremely large size of typical seismic imaging problems has been one of the major stumbling blocks for iterative techniques to attain accurate migration amplitudes. These iterative methods are important because they complement theoretical approaches that are hampered by difficulties to control problems such as finite-acquisition aperture, source-receiver frequency response, and directivity. To solve these problems, we apply preconditioning, which significantly improves convergence of least-squares migration. We discuss different levels of preconditioning that range from corrections for the order of the migration operator to corrections for spherical spreading, and position and reflector-dip dependent amplitude errors. While the first two corrections correspond to simple scalings in the Fourier and physical domain, the third correction requires phase-space (space spanned by location and dip) scaling, which we carry out with curvelets. We show that our combined preconditioner leads to a significant improvement of the convergence of least-squares {\textquoteleft}wave-equation{\textquoteright} migration on a line from the SEG AA{\textquoteright} salt model.},
	keywords = {Imaging, migration, SLIM},
	doi = {10.1190/1.3124753},
	url = {http://geophysics.geoscienceworld.org/content/74/4/A41.abstract},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2009/herrmann2009GEOPcbm/herrmann2009GEOPcbm.pdf},
	author = {Felix J. Herrmann and Cody R. Brown and Yogi A. Erlangga and Peyman P. Moghaddam}
}
@techreport {tang09TRdtr,
	title = {Design of two-dimensional randomized sampling schemes for curvelet-based sparsity-promoting seismic data recovery},
	number = {TR-EOAS-2009-3},
	year = {2009},
	institution = {UBC Earth and Ocean Sciences Department},
	abstract = {The tasks of sampling, compression and reconstruction are very common and often necessary in seismic data processing due to the large size of seismic data. Curvelet-based Recovery by Sparsity-promoting Inversion, motivated by the newly developed theory of compressive sensing, is among the best recovery strategies for seismic data. The incomplete data input to this curvelet-based recovery is determined by randomized sampling of the original complete data. Unlike usual regular undersampling, randomized sampling can convert aliases to easy-to-eliminate noise, thus facilitating the process of reconstruction of the complete data from the incomplete data. Randomized sampling methods such as jittered sampling have been developed in the past that are suitable for curvelet-based recovery, however most have only been applied to sampling in one dimension. Considering that seismic datasets are usually higher dimensional and extremely large, in the present paper, we extend the 1D version of jittered sampling to two dimensions, both with underlying Cartesian and hexagonal grids. We also study separable and non-separable two dimensional jittered sampling, the former referring to the Kronecker product of two one-dimensional jittered samplings. These different categories of jittered sampling are compared against one another in terms of signal-to-noise ratio and visual quality, from which we find that jittered hexagonal sampling is better than jittered Cartesian sampling, while fully non-separable jittered sampling is better than separable sampling. Because in the image processing and computer graphics literature, sampling patterns with blue-noise spectra are found to be ideal to avoid aliasing, we also introduce two other randomized sampling methods, possessing sampling spectra with beneficial blue noise characteristics, Poisson Disk sampling and Farthest Point sampling. We compare these methods, and apply the introduced sampling methodologies to higher dimensional curvelet-based reconstruction. These sampling schemes are shown to lead to better results from CRSI compared to the other more traditional sampling protocols, e.g. regular subsampling.},
	url = {https://slim.gatech.edu/Publications/Public/Journals/2009/tang09TRdtr/tang09TRdtr.pdf},
	author = {Gang Tang and Reza Shahidi and Jianwei Ma}
}
@conference {tang2009SEGhdb,
	title = {Higher dimensional blue-noise sampling schemes for curvelet-based seismic data recovery},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {191-195},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In combination with compressive sensing, a successful reconstruction scheme called Curvelet-based Recovery by Sparsity-promoting Inversion (CRSI) has been developed, and has proven to be useful for seismic data processing. One of the most important issues for CRSI is the sampling scheme, which can greatly affect the quality of reconstruction. Unlike usual regular undersampling, stochastic sampling can convert aliases to easy-to-eliminate noise. Some stochastic sampling methods have been developed for CRSI, e.g. jittered sampling, however most have only been applied to 1D sampling along a line. Seismic datasets are usually higher dimensional and very large, thus it is desirable and often necessary to develop higher dimensional sampling methods to deal with these data. For dimensions higher than one, few results have been reported, except uniform random sampling, which does not perform well. In the present paper, we explore 2D sampling methodologies for curvelet-based reconstruction, possessing sampling spectra with blue noise characteristics, such as Poisson Disk sampling, Farthest Point Sampling, and the 2D extension of jittered sampling. These sampling methods are shown to lead to better recovery and results are compared to the other more traditional sampling protocols.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255230},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/tang09SEGhdb/tang09SEGhdb.pdf},
	author = {Gang Tang and Reza Shahidi and Felix J. Herrmann and Jianwei Ma}
}
@conference {vandenberg2009SLIMocf,
	title = {Optimizing costly functions with simple constraints: a limited-memory projected Quasi-Newton algorithm},
	booktitle = {SLIM},
	series = {Twelfth International Conference on Artificial Intelligence and Statistics},
	volume = {12},
	year = {2009},
	month = {04},
	abstract = {An optimization algorithm for minimizing a smooth function over a convex set is described. Each iteration of the method computes a descent direction by minimizing, over the original constraints, a diagonal-plus low-rank quadratic approximation to the function. The quadratic approximation is constructed using a limited-memory quasi-Newton update. The method is suitable for large-scale problems where evaluation of the function is substan- tially more expensive than projection onto the constraint set. Numerical experiments on one-norm regularized test problems indicate that the proposed method is competitve with state-of-the-art methods such as bound-constrained L-BFGS and orthant-wise descent. We further show that the method generalizes to a wide class of problems, and substantially improves on state-of-the-art methods for problems such as learning the structure of Gaussian graphi- cal models (involving positive-definite matrix constraints) and Markov random fields (involving second-order cone constraints).},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/ mpf/papers/SchmidtBergFriedMurph09.pdf},
	author = {Ewout van den Berg and Mark Schmidt and Michael P. Friedlander and K. Murphy}
}
@conference {moghaddam2008SEGcbm,
	title = {Curvelet-based migration preconditioning},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2211-2215},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we introduce a preconditioner for seismic imaging{\textendash}-i.e., the inversion of the linearized Born scattering operator. This preconditioner approximately corrects for the {\textquoteleft}{\textquoteleft}square root{\textquoteright}{\textquoteright} of the normal{\textendash}-i.e., the demigration-migration operator. This approach consists of three parts, namely (i) a left preconditoner, defined by a fractional time integration designed to make the migration operator zero order, and two right preconditioners that apply (ii) a scaling in the physical domain accounting for a spherical spreading, and (iii) a curvelet-domain scaling that corrects for spatial and reflector-dip dependent amplitude errors. We show that a combination of these preconditioners lead to a significant improvement of the convergence for iterative least-squares solutions to the seismic imaging problem based on reverse-time migration operators.},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.3059325},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/moghaddam08SEGcbm/moghaddam08SEGcbm_pres.pdf},
	author = {Peyman P. Moghaddam and Cody R. Brown and Felix J. Herrmann}
}
@article {herrmann2008GEOPcbs,
	title = {Curvelet-based seismic data processing: a multiscale and nonlinear approach},
	journal = {Geophysics},
	volume = {73},
	number = {1},
	year = {2008},
	month = {03},
	pages = {A1-A5},
	publisher = {SEG},
	abstract = {Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little {\textquoteleft}{\textquoteleft}plane waves,{\textquoteright}{\textquoteright} complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20\%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet{\textquoteright}s ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology. \copyright2008 Society of Exploration Geophysicists},
	keywords = {Acquisition, curvelet transform, Processing, SLIM},
	doi = {10.1190/1.2799517},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2008/herrmann08GEOcbs/herrmann08GEOcbs.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2008SEGcdm,
	title = {Curvelet-domain matched filtering},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	number = {3643-3649},
	year = {2008},
	month = {08},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Matching seismic wavefields and images lies at the heart of many pre-/post-processing steps part of seismic imaging{\textendash}- whether one is matching predicted wavefield components, such as multiples, to the actual to-be-separated wavefield components present in the data or whether one is aiming to restore migration amplitudes by scaling, using an image-to-remigrated-image matching procedure to calculate the scaling coefficients. The success of these wavefield matching procedures depends on our ability to (i) control possible overfitting, which may lead to accidental removal of energy or to inaccurate image-amplitude corrections, (ii) handle data or images with nonunique dips, and (iii) apply subsequent wavefield separations or migraton amplitude corrections stably. In this paper, we show that the curvelet transform allows us to address all these issues by imposing smoothness in phase space, by using their capability to handle conflicting dips, and by leveraging their ability to represent seismic data and images sparsely. This latter property renders curvelet-domain sparsity promotion an effective prior.},
	keywords = {Presentation, SEG, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/herrmann08SEGcdm/herrmann08SEGcdm_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and Deli Wang}
}
@conference {modzelewski2008SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {The SLIM group is actively developing software for seismic imaging. This talk will give a general overview of the software development during SINBAD project with focus on the final release in February 2008. The covered topics will include: 1) adopting Python for object-oriented programming, 2) including parallelism into the algorithms used in seismic imaging/modeling, 3) in-house algorithms for seismic imaging, and 4) contributions to Madagascar (RSF). The talk will serve as an introduction to the other presentations in the session "SINBAD Software releases".},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/modzelewski2008SINBADdas/modzelewski2008SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@techreport {vandenberg08gsv,
	title = {Group sparsity via linear-time projection},
	number = {TR-2008-09},
	year = {2008},
	month = {06},
	institution = {UBC - Department of Computer Science},
	abstract = {We present an efficient spectral projected-gradient algorithm for optimization subject to a group one-norm constraint. Our approach is based on a novel linear-time algorithm for Euclidean projection onto the one- and group one-norm constraints. Numerical experiments on large data sets suggest that the proposed method is substantially more efficient and scalable than existing methods.},
	keywords = {Optimization, SLIM},
	url = {http://www.optimization-online.org/DB_FILE/2008/07/2056.pdf},
	author = {Ewout van den Berg and Mark Schmidt and Michael P. Friedlander and K. Murphy}
}
@conference {maysami2008SEGlcf,
	title = {Lithological constraints from seismic waveforms: application to opal-A to opal-CT transition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {27},
	year = {2008},
	month = {11},
	pages = {2011-2015},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we present a new method for seismic waveform characterization whose aim is threefold, namely (i) extraction of detailed information on the sharpness of transitions in the subsurface from seismic waveforms, (ii) reflector modeling, based on binary-mixture and percolation theory, and (iii) establishment of well-seismic ties, through parameterizations of our waveform and critical reflector model. We test this methodology on the opal-A (Amorphous) to opal-CT (Cristobalite/Tridymite) transition imaged in a migrated section of North Sea field data West of the Shetlands.},
	keywords = {SEG, SLIM},
	doi = {10.1190/1.3059400},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2008/maysami08SEGlcf/maysami08SEGlcf.pdf},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
@mastersthesis {maysami08THlcs,
	title = {Lithology constraints from seismic waveforms: application to opal-A to opal-CT transition},
	year = {2008},
	note = {(MSc)},
	month = {02},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {In this work, we present a new method for seismic waveform characterization, which is aimed at extracting detailed litho-stratigraphical information from seismic data. We attempt to estimate the lithological attributes from seismic data according to our parametric representation of stratigraphical horizons, where the parameter values provide us with a direct link to nature of lithological transitions. We test our method on a seismic dataset with a strong diagenetic transition (opal-A to opal-CT transition). Given some information from cutting samples of well, we use a percolation-based model to construct the elastic profile of lithological transitions. Our goal is to match parametric representation for the diagenetic transition in both real data and synthetic data given by these elastic profiles. This match may be interpreted as a well-seismic tie, which reveals lithological information about stratigraphical horizons.},
	keywords = {MSc},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/maysami08THlcs.pdf},
	author = {Mohammad Maysami}
}
@article {tang2008CITEocs,
	title = {Optimized Compressed Sensing for Curvelet-based Seismic Data Reconstruction},
	journal = {CiteSeer},
	year = {2008},
	abstract = {Compressed sensing (CS) or compressive sampling provides a new sampling theory to reduce data acquisition, which says that compressible signals can be exactly reconstructed from highly incomplete sets of measurements. Very recently, the CS has been applied for seismic exploration and started to compact the traditional data acquisition. In this paper, we present an optimized sampling strategy for the CS data acquisition, which leads to better performance by the curvelet sparsity-promoting inversion in comparison with random sampling and jittered sampling scheme. One of motivation is to reduce the mutual coherence between measurement sampling schemes and curvelet sparse transform in the CS framework. The basic idea of our optimization is to directly redistribute the energy in frequency domain making original spectrum easily discriminated from the random noise induced by random undersampling, while offering control on the maximum gap size. Numerical experiments on synthetic and real seismic data show good performances of the proposed optimized CS for seismic data reconstruction.},
	keywords = {curvelet transform, Optimized Compressive Sensing},
	url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.154.7994},
	url2 = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.154.7994\&rep=rep1\&type=pdf},
	author = {Wen Tang and Jianwei Ma and Felix J. Herrmann}
}
@conference {moghaddam2008SINBADrtm,
	title = {Reverse-time migration amplitude recovery with curvelets},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigenvectors. Subsequently, we propose an approximate nonlinear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/moghaddam2008SINBADrtm/moghaddam2008SINBADrtm.pdf},
	author = {Peyman P. Moghaddam}
}
@manual {rossross08MNsai,
	title = {SLIMPy: a python interface for unix-pipe based coordinate-free scientific computing},
	year = {2008},
	month = {07},
	abstract = {SLIMpy is a Python interface that exposes the functionality of seismic data processing packages, such as MADAGASCAR, through oper ator overloading. SLIMpy provides a concrete coordinate-free implementation of classes for out-of-core linear (implicit matrix-vector), and element-wise operations, including calculation of norms and other basic vector operations. The library is intended to provide the user with an abstract sc ripting language to program iterative algorithms from numerical linear algebra. These algorithms require repeated evaluation of operators that were initially designed to be run as part of batch-oriented processing flows. The current implementation supports a plugin for Madagascar{\textquoteright}s out-of-core UNIX pipe-based applications and is extenable to pipe-based collections of programs such as Seismic Unix, SEPLib, and FreeUSP. To optimize perform ance, SLIMpy uses an Abstract Syntax Tree that parses the algorithm and optimizes the pipes.},
	url = {https://slim.gatech.edu/Software/SLIM/SLIMpy/},
	author = {Sean Ross-Ross and Henryk Modzelewski and Felix J. Herrmann}
}
@article {herrmann2008ACHAsac,
	title = {Sparsity- and continuity-promoting seismic image recovery with curvelet frames},
	journal = {Applied and Computational Harmonic Analysis},
	volume = {24},
	number = {2},
	year = {2008},
	month = {03},
	pages = {150-173},
	abstract = {A nonlinear singularity-preserving solution to seismic image recovery with sparseness and continuity constraints is proposed. We observe that curvelets, as a directional frame expansion, lead to sparsity of seismic images and exhibit invariance under the normal operator of the linearized imaging problem. Based on this observation we derive a method for stable recovery of the migration amplitudes from noisy data. The method corrects the amplitudes during a post-processing step after migration, such that the main additional cost is one ap- plication of the normal operator, i.e. a modeling followed by a migration. Asymptotically this normal operator corresponds to a pseudodifferential operator, for which a convenient diagonal approximation in the curvelet domain is derived, including a bound for its error and a method for the estimation of the diagonal from a compound operator consisting of discrete implementations for the scattering operator and its adjoint the migration operator. The solution is formulated as a nonlinear optimization problem where sparsity in the curvelet domain as well as continuity along the imaged reflectors are jointly promoted. To enhance sparsity, the $ell_1$-norm on the curvelet coefficients is minimized, while continuity is promoted by minimizing an anisotropic diffusion norm on the image. The performance of the recovery scheme is evaluated with a time-reversed {\textquoteright}wave-equation{\textquoteright} migration code on synthetic datasets, including the complex SEG/EAGE AA salt model.},
	keywords = {curvelet transform, Imaging, Processing, SLIM},
	doi = {10.1016/j.acha.2007.06.007},
	url = {https://slim.gatech.edu/Publications/Public/Journals/ACHA/2008/herrmann2008ACHAsac/herrmann2008ACHAsac.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam and Christiaan C. Stolk}
}
@conference {herrmann2007EAGEjda,
	title = {Just diagonalize: a curvelet-based approach to seismic amplitude recovery},
	booktitle = {EAGE Workshop on Curvelets, contourlets, seislets, {\textellipsis} in seismic data processing - where are we and where are we going?},
	year = {2007},
	month = {06},
	abstract = {In his presentation we present a nonlinear curvelet-based sparsity-promoting formulation for the recovery of seismic amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under wave propagation lead to a formulation of this recovery problem that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann2007EAGEjda/herrmann2007EAGEjda_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7555},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {moghaddam2007CSEGmar,
	title = {Migration amplitude recovery using curvelets},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2007},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {In this paper, we recover the amplitude of a seismic image by approximating the normal operator and subsequently inverting it. Normal operator (migration followed by modeling) is an example of pseudo-differential. curvelets are proven to be invariant under the action of pseudo-differential operators under certain conditions. Subsequently, curvelets are forming as eigen-vectors for such an operator. We propose a seismic amplitude recovery method that employs an eigen-value decomposition for normal operator using curvelets as eigen-vectors and to be estimated eigenvalues. A post-stack reverse-time, wave-equation migration is used for evaluation of the proposed method.},
	keywords = {SLIM},
	url = {http://cseg.ca/assets/files/resources/abstracts/2007/168S0131.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {moghaddam2007SEGrsi,
	title = {Robust seismic-images amplitude recovery using curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2225-2229},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we recover the amplitude of a seismic image by approximating the normal (demigration-migration) operator. In this approximation, we make use of the property that curvelets remain invariant under the action of the normal operator. We propose a seismic amplitude recovery method that employs an eigenvalue like decomposition for the normal operator using curvelets as eigen-vectors. Subsequently, we propose an approximate non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness in the curvelet domain and spatial continuity constraints. Our method is tested with a reverse-time {\textquoteleft}wave-equation{\textquoteright} migration code simulating the acoustic wave equation on the SEG-AA salt model. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792928},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/moghaddam07SEGrsi/moghaddam07SEGrsi_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {moghaddam2007EAGEsar,
	title = {Seismic amplitude recovery with curvelets},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {A non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness and continuity constraints is proposed. The applied formalism explores curvelets as a directional frame that, by their sparsity on the image, and their invariance under the imaging operators, allows for a stable recovery of the amplitudes. Our method is based on the estimation of the normal operator in the form of an {\textquoteright}eigenvalue{\textquoteright} decompsoition with curvelets as the {\textquoteright}eigenvectors{\textquoteright}. Subsequently, we propose an inversion method that derives from estimation of the normal operator and is formulated as a convex optimization problem. Sparsity in the curvelet domain as well as continuity along the reflectors in the image domain are promoted as part of this optimization. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/moghaddam07EAGEsar/moghaddam07EAGEsar.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=6935},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {herrmann2007SEGsdp,
	title = {Seismic data processing with curvelets: a multiscale and nonlinear approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2220-2224},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation of a seismic processing flow, consisting of the following steps: seismic data regularization and the restoration of migration amplitudes. We show that the curvelet{\textquoteright}s wavefront detection capability and invariance under the migration-demigration operator lead to a formulation that is stable under noise and missing data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792927},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/herrmann07SEGsdp/herrmann07SEGsdp_pres.pdf},
	author = {Felix J. Herrmann and Deli Wang and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {herrmann2007EAGEsia,
	title = {Seismic imaging and processing with curvelets},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {In this paper, we present a nonlinear curvelet-based sparsity-promoting formulation for three problems in seismic processing and imaging namely, seismic data regularization from data with large percentages of traces missing; seismic amplitude recovery for sub-salt images obtained by reverse-time migration and primary-multiple separation, given an inaccurate multiple prediction. We argue why these nonlinear formulations are beneficial.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGEsia/herrmann07EAGEsia_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7075},
	author = {Felix J. Herrmann and Gilles Hennenfent and Peyman P. Moghaddam}
}
@conference {maysami2007EAGEsrc,
	title = {Seismic reflector characterization by a multiscale detection-estimation method},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Seismic transitions of the subsurface are typically considered as zero-order singularities (step functions). According to this model, the conventional deconvolution problem aims at recovering the seismic reflectivity as a sparse spike train. However, recent multiscale analysis on sedimentary records revealed the existence of accumulations of varying order singularities in the subsurface, which give rise to fractional-order discontinuities. This observation not only calls for a richer class of seismic reflection waveforms, but it also requires a different methodology to detect and characterize these reflection events. For instance, the assumptions underlying conventional deconvolution no longer hold. Because of the bandwidth limitation of seismic data, multiscale analysis methods based on the decay rate of wavelet coefficients may yield ambiguous results. We avoid this problem by formulating the estimation of the singularity orders by a parametric nonlinear inversion method.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/maysami07EAGEsrc/maysami07EAGEsrc_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7081},
	author = {Mohammad Maysami and Felix J. Herrmann}
}
@manual {rossross07MNsda,
	title = {SLIMpy development and programming interface for seismic processing},
	year = {2007},
	abstract = {Inverse problems in (exploration) seismology are known for their large to very large scale. For instance, certain sparsity-promoting inversion techniques involve vectors that easily exceed unknowns while seismic imaging involves the construction and application of matrix-free discretized operators where single matrix-vector evaluations may require hours, days or even weeks on large compute clusters. For these reasons, software development in this field has remained the domain of highly technical codes programmed in low-level languages with little eye for easy development, code reuse and integration with (nonlinear) programs that solve inverse problems.Following ideas from the Symes{\textquoteright} Rice Vector Library and Bartlett{\textquoteright}s C++ object-oriented interface, Thyra, and Reduction/Transformation operators (both part of the Trilinos software package), we developed a software-development environment based on overloading. This environment provides a pathway from in-core prototype development to out-of-core and MPI {\textquoteright}production{\textquoteright} code with a high level of code reuse. This code reuse is accomplished by integrating the out-of-core and MPI functionality into the dynamic object-oriented programming language Python. This integration is implemented through operator overloading and allows for the development of a coordinate-free solver framework that (i) promotes code reuse; (ii) analyses the statements in an abstract syntax tree and (iii) generates executable statements. In the current implementation, we developed an interface to generate executable statements for the out-of-core unix-pipe based (seismic) processing package RSF-Madagascar (rsf.sf.net). The modular design allows for interfaces to other seismic processing packages and to in-core Python packages such as numpy. So far, the implementation overloads linear operators and element-wise reduction/transformation operators. We are planning extensions towards nonlinear operators and integration with existing (parallel) solver frameworks such as Trilinos.},
	keywords = {SLIM, software},
	url = {https://slim.gatech.edu/Software/SLIM/SLIMpy/},
	author = {Sean Ross-Ross and Henryk Modzelewski and Cody R. Brown and Felix J. Herrmann}
}
@conference {moghaddam2007CSEGsac,
	title = {Sparsity and continuity enhancing seismic imaging},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2007},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {A non-linear singularity-preserving solution to the least-squares seismic imaging problem with sparseness and continuity constraints is proposed. The applied formalism explores curvelets as a directional frame that, by their sparsity on the image, and their invariance under the imaging operators, allows for a stable recovery of the amplitudes. Our method is based on the estimation of the normal operator in the form of an {\textquoteright}eigenvalue{\textquoteright} decompsoition with curvelets as the eigenvectors{\textquoteright}. Subsequently, we propose an inversion method that derives from estimation of the normal operator and is formulated as a convex optimization problem. Sparsity in the curvelet domain as well as continuity along the reflectors in the image domain are promoted as part of this optimization. Our method is tested with a reverse-time {\textquoteright}wave-equation{\textquoteright} migration code simulating the acoustic wave equation.},
	keywords = {SLIM},
	url = {http://cseg.ca/assets/files/resources/abstracts/2007/091S0130.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann and Christiaan C. Stolk}
}
@conference {modzelewski2006SINBADdas,
	title = {Design and specifications for SLIM{\textquoteright}s software framework},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/modzelewski2006SINBADdas/modzelewski2006SINBADdas.pdf},
	author = {Henryk Modzelewski}
}
@conference {moghaddam2006SINBADioa,
	title = {Imaging operator approximation using curvelets},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {In this presentation, the normal (demigation-migration) operator is studied in terms of a pseudo-differential operator. The invariance of curvelets under this operator and their sparsity on the seismic images is used to precondition the migration operator. A brief overview will be given on some of the theory from micro-local analysis which proofs that curvelets remain approximately invariant under the operator. The proper setting for which a diagonal approximation in the curvelet domain is accurate is discussed together with different methods that estimate this diagonal from of-the-shelf migration operators. This is joint work with Chris Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADioa/moghaddam2006SINBADioa.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {thomson2006SEGpwfd,
	title = {A parallel windowed fast discrete curvelet transform applied to seismic processing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {25},
	year = {2006},
	pages = {2767-2771},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear oper ators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallel processing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we apply to a seismic data interpolation algorithm. The successful performance of our parallel processing scheme and algorithm on a two-dimensional synthetic data is shown.},
	keywords = {SEG},
	doi = {10.1190/1.2370099},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/thomson06SEGpwfd/thomson06SEGpwfd.pdf},
	author = {Darren Thomson and Gilles Hennenfent and Henryk Modzelewski and Felix J. Herrmann}
}
@conference {maysami2006SINBADrro,
	title = {Recent results on seismic deconvolution},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {One of the important steps in seismic imaging is to provide suitable information about boundaries. Sharp variation of physical properties at a layer boundary cause reflection the wavefield. In previous work done by C. M. Dupuis, seismic signal characterization is divided into two steps: detection and estimation. In the detection phase, the goal is to find all singularities in a seismic section regardless of their order and then to categorize the data to different events by windowing each singularity. In the estimation step, we determine the order of singularity more precisely by using a rough estimate based on the detection phase. Traditionally, a redundant dictionary method is employed for the detection part. However, we attempt to instead use a new L1-solver developed by D.L. Donoho: the Stagewise Orthogonal Matching Pursuit (StOMP). It approximates the solution to inverse problems while promoting the sparsity in the solution vector. This algorithm will allow us to experimentally confirm the recent analysis by S. Mallat on spiky deconvolution limits, which imposes a required minimum distance between spikes. This required minimum distance between different spikes is dependent on the number of spikes as well as the width of the chosen source wavelet used in convolution with the train. These results allow for the design of more robust and accurate detection schemes for seismic signal characterization.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/maysami06SINBADrro/maysami06SINBADrro_pres.pdf},
	author = {Mohammad Maysami}
}
@conference {moghaddam2006SINBADsac,
	title = {Sparsity- and continuity-promoting norms for seismic images},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation, the importance of sparsity and continuity enhancing energy norms is emphasized for seismic imaging and inversion. The continuity promoting energy norm is justified by the apparent smoothness of reflectors in the direction along and the oscillatory behavior across the interfaces. This energy norm is called anisotropic diffusion and will be defined mathematically. Denoising examples will be given during which seismic images are recovered from the noise by a joint norm-one and continuity promoting minimization.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/moghaddam2006SINBADsac/moghaddam2006SINBADsac.pdf},
	author = {Peyman P. Moghaddam}
}
@conference {herrmann2005CSEGnlr,
	title = {Non-linear regularization in seismic imaging},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2005},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {Two complementary solution strategies to the least-squares imaging problem with sparseness \& continuity continuity constraints are proposed. The applied formalism explores the sparseness of curvelets coefficients of the reflectivity and their invariance under the demigration-migration operator. We achieve the solution by jointly minimizing a weighted l1-norm on the curvelet coefficients and an anisotropic difussion or total variation norm on the imaged reflectivity model. The l1-norm exploits the sparsenss of the reflectivity in the curvelet domain whereas the anisotropic norm enhances the continuity along the reflections while removing artifacts residing in between reflectors. While the two optimization methods (convex versus non-convex) share the same type of regularization, they differ in flexibility how to handle additional constraints on the coefficients of the imaged reflectivity and in computational expense. A brief sketch of the theory is provided along with a number of synthetic examples.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2005/Herrmann05CSEGnlr/Herrmann05CSEGnlr_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@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}
}
@conference {herrmann2004CSEGcia2,
	title = {Curvelet imaging and processing: sparseness-constrained least-squares migration},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2004},
	month = {05},
	publisher = {CSEG},
	organization = {CSEG},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is, like-wise to the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2004/Herrmann04CSEGcia2/Herrmann04CSEGcia2.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004SEGcbn,
	title = {Curvelet-based non-linear adaptive subtraction with sparseness constraints},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {1977-1980},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper an overview is given on the application of directional basis functions, known under the name Curvelets/Contourlets, to various aspects of seismic processing and imaging, which involve adaptive subtraction. Key concepts in the approach are the use of directional basis functions that localize in both domains (e.g. space and angle); non-linear estimation, which corresponds to localized muting on the coefficients, possibly supplemented by constrained optimization. We will discuss applications that include multiple, ground-roll removal and migration denoising. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1851181},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Herrmann04SEGcbn/Herrmann04SEGcbn_pres.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004EAGEcdl,
	title = {Curvelet-domain least-squares migration with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal-to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2004/Herrmann04EAGEcdl/Herrmann04EAGEcdl_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=2073},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {herrmann2004EAGEcdp,
	title = {Curvelet-domain preconditioned {\textquoteright}wave-equation{\textquoteright} depth-migration with sparseness and illumination constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2004},
	month = {06},
	abstract = {A non-linear edge-preserving solution to the least-squares migration problem with sparseness constraints is introduced. The applied formalism explores Curvelets as basis functions that, by virtue of their sparseness and locality, not only allow for a reduction of the dimensionality of the imaging problem but which also naturally lead to a non-linear solution with significantly improved signal- to-noise ratio. Additional conditions on the image are imposed by solving a constrained optimization problem on the estimated Curvelet coefficients initialized by thresholding. This optimization is designed to also restore the amplitudes by (approximately) inverting the normal operator, which is like-wise the (de)-migration operators, almost diagonalized by the Curvelet transform.},
	keywords = {SLIM},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2004/herrmann2004EAGEcdp/herrmann2004EAGEcdp.pdf},
	author = {Felix J. Herrmann and Peyman P. Moghaddam}
}
@conference {moghaddam2004SEGmpw,
	title = {Migration preconditioning with curvelets},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {23},
	year = {2004},
	pages = {2204-2207},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, the property of Curvelet transforms for preconditioning the migration and normal operators is investigated. These operators belong to the class of Fourier integral operators and pseudo-differential operator, respectively. The effect of this pre-conditioner is shown in term of improvement of sparsity, convergence rate, number of iteration for the Krylov-subspace solver and clustering of singular(eigen) values. The migration operator, which we employed in this work is the common-offset Kirchoff-Born migration. \copyright2004 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.1845213},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_paper.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2004/Moghaddam04SEGmpw/Moghaddam04SEGmpw_pres.pdf},
	author = {Peyman P. Moghaddam and Felix J. Herrmann}
}
@article {bernabe2004JGRpas,
	title = {Permeability and storativity of binary mixtures of high-and low-porosity materials},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {109},
	year = {2004},
	month = {12},
	pages = {B12207},
	abstract = {As a first step toward determining the mixing laws for the transport properties of rocks, we prepared binary mixtures of high- and low-permeability materials by isostatically hot-pressing mixtures of fine powders of calcite and quartz. The resulting rocks were marbles containing varying concentrations of dispersed quartz grains. Pores were present throughout the rock, but the largest ones were preferentially associated with the quartz particles, leading us to characterize the material as being composed of two phases, one with high permeability and the second with low permeability. We measured the permeability and storativity of these materials using the oscillating flow technique, while systematically varying the effective pressure and the period and amplitude of the input fluid oscillation. Control measurements performed using the steady state flow and pulse decay techniques agreed well with the oscillating flow tests. The hydraulic properties of the marbles were highly sensitive to the volume fraction of the high-permeability phase (directly related to the quartz content). Below a critical quartz content, slightly less than 20 wt \%, the high-permeability volume elements were disconnected, and the overall permeability was low. Above the critical quartz content the high-permeability volume elements formed throughgoing paths, and permeability increased sharply. We numerically simulated fluid flow through binary materials and found that permeability approximately obeys a percolation-based mixing law, consistent with the measured permeability of the calcite-quartz aggregates.},
	keywords = {Modeling, permeability, porosity, SLIM},
	doi = {10.1029/2004JB003111},
	url = {https://slim.gatech.edu/Publications/Public/Journals/JournalOfGeophysicalResearch/2004/bernabe04JGRpas/bernabe04JGRpas.pdf},
	url2 = {http://onlinelibrary.wiley.com/doi/10.1029/2004JB003111/abstract},
	author = {Y. Bernab{\'e} and U. Mok and B. Evans and Felix J. Herrmann}
}
