@conference {zeng2025ML4SEISMICrrs,
	title = {Reconstructing reservoir states from multimodal data via score-based generative models},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {8},
	abstract = {This study develops a score-based generative framework for reservoir simulation to reconstruct spatially varying permeability and saturation distributions in saline aquifers from sparse observations at two wells combined with time-lapse seismic data. We learn joint distributions of permeability, saturation, and pressure from high-fidelity multiphase-flow simulations, conditioned on observed imaged time-lapse seismic data. The framework provides a unified, any-subset inference interface that flexibly incorporates well logs and physics-based constraints to enforce mass balance and physically plausible behavior. An ablation without pressure on training yields markedly lower structural similarity within physically meaningful measurement ranges, underscoring the pressure{\textquoteright}s importance for accurate reconstruction of the saturation. Our approach generalizes across varying geological settings and highlights the value of multimodal data fusion for practical reservoir management.},
	keywords = {deep learning, diffusion-pde models, Generative models, history matching, ML4SEISMIC, reservoir simulation, score, SLIM, sparse observations, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/zeng2025ML4SEISMICrrs},
	author = {Shiqin Zeng and Abhinav Prakash Gahlot and Haoyun Li and Felix J. Herrmann}
}
@conference {park2025IMAGErdn,
	title = {A reduced-order derivative-informed neural operator for subsurface fluid-flow},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, derivatve-informed, Fisher-information, FNO, GCS, IMAGE, Inverse problems, permeability, reduced-order modeling, reservoir simulation, SEG, Uncertainty quantification},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/park2025IMAGErdn/IMAGE_Jayjay.pdf},
	author = {Jeongjin Park and Grant Bruer and Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {li2025ML4SEISMICric,
	title = {Risk-Aware Injectivity Control for Energy Storage},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {We introduce a digital-twin{\textendash}driven framework for real-time injectivity control that maximizes injected CO2 while enforcing reservoir safety. The forward model is a multiphase flow simulator evolving reservoir states under injectivity controls q; time-lapse seismic acts as the observation operator to reduce uncertainty of the the state (reservoir{\textquoteright}s saturation/pressure). Risk is handled via two complementary metrics: the Probability of Failure (POF) for exceedance likelihood and the less conservative CVaR (superquantile) for tail severity. For the optimization, we employ smooth surrogates{\textemdash}a logistic exceedance rate for POF and the Rockafellar{\textendash}Uryasev formulation for CVaR{\textemdash}enabling gradient-based control with constraints. Within a finite-horizon Model Predictive Control (MPC) loop, we optimize a trajectory per posterior sample and aggregate the ensemble of optimal rates into an empirical (KDE-kernel) Cumulative Density Function (CDF) with binomial confidence bands; the final controlled injectivity q is chosen as the largest rate whose upper confidence bound satisfies the target risk level. Case studies demonstrate that POF-constrained optimization is more conservative than CVaR-based optimization, In addition, sensitivity of the policy to the CVaR tail level, α, is reduced compared to POF{\textquoteright}s parameter sensitivity, allowing for adaptive, uncertainty-aware control that balances injectivity and fracture risk. The proposed framework also unifies time-lapse seismic-informed inference, risk metrics, and MPC into a practical approach for safe high-throughput underground energy storage.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, EOR, experimental design, FWI, GCS, Imaging, Inverse problems, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/li2025ML4SEISMICric},
	author = {Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {herrmann2025SIAMrdt,
	title = {Robust Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Computational Science and Engineering (CSE25)},
	year = {2025},
	note = {(SIAM CSE25, Dallas)},
	month = {03},
	abstract = {While recent work has shown that conditional neural networks can be used to effectuate prior-to-posterior mappings during ensemble Kalman filtering, their accuracy relies on the quality of the training ensemble, consisting of the simulated time-advanced nonlinear state and possibly nonlinear observations. Its precision also depends on the generative neural networks{\textquoteright} ability to generalize. In this work, we will report progress on how to make our Digital Twin more robust with respect to distribution shifts and modeling errors, e.g. due to erroneous choices for the physics within the Forecast Step. To this end, we will follow a two-pronged approach consisting of augmenting the Forecast step, e.g. by including different models for the rock physics that map the Digital Twin{\textquoteright}s state to seismic properties, and by adding more physics during the prior-to-posterior mapping. In its current implementation, training of our Digital Twin{\textquoteright}s neural networks relies on relative low-fidelity amortization of the Forecast Ensemble, which is susceptible to distribution shifts. By conducting non-amortized inference informed by the physics and low-fidelity prior-to-posterior mapping, we propose an approach that is more robust than the current implementation of the Digital Twin.},
	keywords = {Bayesian inference, conditional normalizing flows, CSE25, digital twin, rock physics, sequential Bayes, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMCSE/2025/herrmann2025SIAMrdt},
	author = {Felix J. Herrmann and Abhinav Prakash Gahlot and Rafael Orozco and Ziyi Yin and Grant Bruer}
}
@presentation {li2024ML4SEISMICrps,
	title = {Reconstructing Permeability and Saturation in Reservoir Simulation Using Diffusion PDE Models},
	year = {2024},
	month = {11},
	abstract = {This study explores the application of a diffusion partial differential equation (PDE) model for reservoir simulation, particularly aimed at reconstructing permeability and saturation fields within a saline aquifer. Focusing on pairs of input permeability and output saturation, the model is trained to capture the underlying dynamics governing fluid flow in porous media. Post-training, the model is capable of inferring or recovering the complete permeability and saturation distributions when provided with limited vertical pixel data of permeability and saturation. This approach offers a novel pathway for enhancing the resolution of subsurface characteristics, contributing to more accurate predictions in reservoir engineering and carbon storage simulations.},
	keywords = {augmentation, Bayesian inference, deep learning, diffusion models, Inverse problems, ML4SEISMIC, SLIM, Summary Statistics, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/li2024ML4SEISMICrps},
	author = {Haoyun Li and Shiqin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {orozco2023AABIiter,
	title = {Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics},
	booktitle = {5th Symposium on Advances in Approximate Bayesian Inference},
	year = {2023},
	note = {(AABI, Honolulu)},
	month = {06},
	abstract = {We present an iterative framework to improve the amortized approximations of posterior distributions in the context of Bayesian inverse problems, which is inspired by loop-unrolled gradient descent methods and is theoretically grounded in maximally informative summary statistics. Amortized variational inference is restricted by the expressive power of the chosen variational distribution and the availability of training data in the form of joint data and parameter samples, which often lead to approximation errors such as the amortization gap. To address this issue, we propose an iterative framework that refines the current amortized posterior approximation at each step. Our approach involves alternating between two steps: (1) constructing a training dataset consisting of pairs of summarized data residuals and parameters, where the summarized data residual is generated using a gradient-based summary statistic, and (2) training a conditional generative model {\textendash} a normalizing flow in our examples {\textendash} on this dataset to obtain a probabilistic update of the unknown parameter. This procedure leads to iterative refinement of the amortized posterior approximations without the need for extra training data. We validate our method in a controlled setting by applying it to a stylized problem, and observe improved posterior approximations with each iteration. Additionally, we showcase the capability of our method in tackling realistically sized problems by applying it to transcranial ultrasound, a high-dimensional, nonlinear inverse problem governed by wave physics, and observe enhanced posterior quality through better image reconstruction with the posterior mean.},
	keywords = {AABI, Amortization Gap, Amortized Variational Inference, Bayesian inference, conditional normalizing flows, Summary Statistics},
	url = {https://arxiv.org/abs/2305.08733},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AABI/2023/orozco2023AABIiter/aabi_poster.pdf},
	author = {Rafael Orozco and Ali Siahkoohi and Mathias Louboutin and Felix J. Herrmann}
}
@article {siahkoohi2022ravi,
	title = {Reliable amortized variational inference with physics-based latent distribution correction},
	journal = {Geophysics},
	volume = {88},
	number = {3},
	year = {2023},
	note = {(Geophysics)},
	month = {05},
	abstract = {Bayesian inference for high-dimensional inverse problems is challenged by the computational costs of the forward operator and the selection of an appropriate prior distribution. Amortized variational inference addresses these challenges where a neural network is trained to approximate the posterior distribution over existing pairs of model and data. When fed previously unseen data and normally distributed latent samples as input, the pretrained deep neural network{\textendash}-in our case a conditional normalizing flow{\textendash}-provides posterior samples with virtually no cost. However, the accuracy of this approach relies on the availability of high-fidelity training data, which seldom exists in geophysical inverse problems because of the highly heterogeneous structure of the Earth. In addition, accurate amortized variational inference requires the observed data to be drawn from the training data distribution. As such, we offer a solution that increases the resilience of amortized variational inference when faced with data distribution shifts, e.g., changes in the forward model or prior distribution. Our method involves a physics-based correction to the conditional normalizing flow latent distribution to provide a more accurate approximation to the posterior distribution for the observed data at hand. To accomplish this, instead of a standard Gaussian latent distribution, we parameterize the latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance. These unknown quantities are then estimated by minimizing the Kullback-Leibler divergence between the corrected and true posterior distributions. While generic and applicable to other inverse problems, by means of a seismic imaging example, we show that our correction step improves the robustness of amortized variational inference with respect to changes in number of source experiments, noise variance, and shifts in the prior distribution. This approach provides a seismic image with limited artifacts and an assessment of its uncertainty with approximately the same cost as five reverse-time migrations.},
	keywords = {Inverse problems, Normalizing flows, seismic imaging, Uncertainty quantification, Variational Inference},
	doi = {10.1190/geo2022-0472.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2023/siahkoohi2022ravi/paper.html},
	software = {https://github.com/slimgroup/ReliableAVI.jl},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@conference {siahkoohi2022SEGWSrav,
	title = {Reliable amortized variational inference with conditional normalizing flows via physics-based latent distribution correction},
	booktitle = {IMAGE Workshop on Subsurface Uncertainty Description and Estimation - Moving Away from Single Prediction with Distribution Learning},
	year = {2022},
	note = {(IMAGE Workshop, Houston)},
	month = {09},
	abstract = {Bayesian inference for high-dimensional inverse problems is challenged by the computational costs associated with the forward operator during posterior sampling, as well as the selection of an appropriate prior distribution that encodes our prior knowledge about the unknown. Amortized variational inference addresses these challenges by pretraining a conditional normalizing flow (cNF) that approximates the posterior distribution over the existing model and data joint samples where the prior is implicitly learned from the data. When fed data and normally distributed latent samples as input, the pretrained cNF provides posterior samples for previously unseen data virtually for free. The accuracy of this purely data-driven approach, however, is dependent on the availability of high-fidelity training data, which is rarely the case with geophysical inverse problems, due to the highly heterogeneous structure of the Earth. To overcome this challenge and to minimize the negative bias of data distribution shifts during inference, we propose learning a physics-based correction to the cNF latent distribution to provide a more accurate approximation to the posterior distribution. We accomplish this by parameterizing the cNF latent distribution by a Gaussian distribution with an unknown mean and diagonal covariance, which are estimated by minimizing the Kullback-Leibler divergence between the corrected posterior distribution estimate and the true posterior distribution. For a relatively well-trained cNF, this approach provides reliable posterior samples with a limited computational cost while remaining bound to data and physics. We showcase the computational gains of this approach on a "quasi" real seismic imaging example.},
	keywords = {Bayesian inference, deep learning, Generative models, Imaging, Inverse problems, SEG, Uncertainty quantification, workshop},
	url = {https://www.imageevent.org/Workshop/subsurface-uncertainty-description-estimation-moving-away-single-prediction-distribution-learning},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/siahkoohi2022SEGWSrav/Thur-15-55-Siahkoohi.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Rafael Orozco and Felix J. Herrmann}
}
@presentation {louboutin2021ML4SEISMICrla,
	title = {Randomized linear algebra for inversion},
	year = {2021},
	month = {11},
	abstract = {Inverse problems in exploration geophysics or machine learning heavily relies on linear algebra and large matrices manipulations. To tackle the growing cost of storing these matrices, randomized algorithms have been developed to obtain information from these matrices via randomized sketching. Inspired by previous work on extended image volumes, we will first show in this talk how the seismic imaging condition can be expressed in a randomized linear algebra framework leading to drastic memory savings. In a second part, we will extend this idea to convolutional neural networks to reduce the memory cost of training by orders of magnitude. We will demonstrate the practicality of these methods on representative examples.},
	keywords = {FWI, HPC, inversion, ML4SEISMIC, randomized linear algebra, SLIM, software},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/louboutin2021ML4SEISMICrla/Tue-10-20-Louboutin.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {witte2021ML4SEISMICrtc,
	title = {Redwood {\textendash} towards clusterless supercomputing in the cloud},
	year = {2021},
	month = {11},
	abstract = {We present Redwood, a Julia framework for clusterless supercomputing in the cloud. Redwood provides a set of distributed programming macros that enable users to remotely execute Julia functions in parallel through cloud services for batch and serverless computing. We present the architecture and design of Redwood, as well as its application to existing Julia packages for machine learning and inverse problems.},
	keywords = {cloud computing, clusterless, HPC, Julia, ML4SEISMIC, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2021/witte2021ML4SEISMICrtc/Mon-10-30-Witte.pdf},
	author = {Philipp A. Witte}
}
@conference {yang2018SEGrde,
	title = {Removing density effects in LS-RTM with a low-rank matched filter},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {4251-4255},
	abstract = {Least-squares reverse-time migration faces difficulties when it inverts the data containing strong components related to density variation with velocity-only Born modeling operator. The strong density perturbation will be inverted as strong dummy velocity perturbations, which influence the amplitudes and phase of the velocity perturbations in the inverted model. The traditional method is to invert the additional density variations by developing Born operator with respect to both density and velocity or modify the image condition. In this work, we develop a matched-filter based LS-RTM for velocity-only Born modeling operator, which removes the artifacts in the imaging created by the strong density variation. This method doesn{\textquoteright}t call for extra work of finite difference stencil and is more general. In the experiment part, we use a complex discontinuous layered medium with strong density variations at the ocean bottom, and show the efficacy of the propose formulation.},
	keywords = {density effect, Least square, low rank, SEG},
	doi = {10.1190/segam2018-2997814.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/yang2018SEGrde/yang2018SEGrde_pres.pdf},
	author = {Mengmeng Yang and Rajiv Kumar and Rongrong Wang and Felix J. Herrmann}
}
@conference {louboutin2017SIAMras,
	title = {Raising the abstraction to separate concerns: enabling different physics for geophysical exploration},
	booktitle = {SIAM Conference on Computational Science and Engineering},
	year = {2017},
	note = {(SIAM Conference on Computational Science and Engineering, Atlanta)},
	month = {02-03},
	abstract = {Full-waveform inversion is a PDE-constrained optimisation problem involving massive amounts of data (petabytes) and large numbers of unknowns (O(10^9)). This well known compute-intensive and data-intensive is extremely challenging for several reasons. First, there is the complexity of having to handle extremely large data volumes with metadata related to experimental details in the field, and the discretization of the unknown earth parameters and approximate physics. Second, reduced or adjoint-state methods call for computationally intensive PDE solves for each source experiment (of which there are thousands) for each iteration of a gradient-based optimization scheme. The talks will give an overview how carefully chosen layers of abstraction can help manage both the complexity and scale of inversion while still achieving the high degree of computational performance required to make full-waveform a practical tool. Specifically, the presentations will focus on domain specific stencil language for time-stepping methods to solve various types of wave equations and on abstracts for large-scale parallel optimization frameworks.},
	keywords = {finite differences, HPC, Modelling, SIAM, time domain},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/louboutin2017SIAMras/louboutin2017SIAMras_pres.mov},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2017/louboutin2017SIAMras/louboutin2017SIAMras_pres.pdf},
	author = {Mathias Louboutin and Michael Lange and Navjot Kukreja and Fabio Luporini and Felix J. Herrmann and Gerard Gorman}
}
@presentation {alfaraj2017SINBADFros,
	title = {Reconstruction of S-waves from low-cost randomized acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier according to the Nyquist sampling criterion. To avoid higher acquisition costs and to utilize the multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. We present two up- and down-going shear wave reconstruction methods: (i) rank minimization reconstruction followed by elastic wavefield decomposition, and (ii) sparsity promoting joint interpolation decomposition using all the multicomponent data in one optimization problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.mov},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2017SEGrsw,
	title = {Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {2533-2538},
	abstract = {Spatial Nyquist sampling rate, which is proportional to the apparent subsurface velocity, is the key deciding cost factor for conventional seismic acquisition. Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier, which increases the acquisition costs. This is one of the reasons why shear waves are usually not considered in practice. To avoid having the Nyquist criterion as the deciding cost factor and to utilize multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. Each component can then be interpolated separately, followed by elastic decomposition to recover up- and down-going S-waves. Instead, we jointly interpolate and decompose the recorded multicomponent data by solving one sparsity promoting optimization problem. This way we ensure that the relative amplitudes of the multicomponent data is preserved. We compare two sparsifying transforms: the curvelet transform and the frequency-wavenumber transform. Another key cost deciding factor for seismic acquisition is the efficiency of acquiring data. This calls for simultaneous acquisition, which requires a source separation step. Similarly, instead of taking a two-step approach, we perform a sparsity-promoting joint source separation decomposition. Results on economically and efficiently acquired synthetic data of both joint methods show their ability of reconstructing accurate up- and down-going S-waves.},
	keywords = {decomposition, elastic, interpolation, randomized acquisition, SEG, shear wave, source separation, sparse},
	doi = {10.1190/segam2017-17681376.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {wason2015SINBADFrtl,
	title = {Randomization of time-lapse marine surveys},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an extension of our time-jittered (simultaneous) marine acquisition for time-lapse surveys, where we work on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we recover the densely sampled baseline and monitor and then the (complete) 4-D difference from subsampled baseline and monitor data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/wason2015SINBADFrtl/wason2015SINBADFrtl.mov},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {herrmann2015ASEGrae,
	title = {Randomized algorithms in exploration seismology},
	booktitle = {ASEG Annual Conference Proceedings},
	year = {2015},
	note = {(ASEG, Perth)},
	month = {02},
	abstract = {As in several other industries, progress in exploration seismology relies on the collection and processing of massive data volumes that grow exponentially in size as the survey area and desired resolution increase. This exponential growth {\textendash}- in combination with the increased complexity of the next-generation of iterative wave-equation based inversion algorithms {\textendash}- puts strains on our acquisition systems and computational back ends impeding progress in our field. During this talk, I will review how recent randomized algorithms from Compressive Sensing and Machine learning can be used to overcome some of these challenges by fundamentally rethinking how we sample and process seismic data. The key idea here is to reduce acquisition and computational costs by deliberately working on small randomized subsets of the data at a desired accuracy. I will illustrate these concepts using a variety of compelling examples on realistic synthetics and field data.},
	keywords = {Acquisition, ASEG, compressed sensing, FWI, Imaging, seismology},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ASEG/2015/herrmann2015ASEGrae/herrmann2015ASEGrae.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2015IIPFWIras,
	title = {Randomized algorithms in seismic imaging},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {As in several other industries, progress in exploration seismology relies on the collection and processing of massive data volumes that grow exponentially in size as the survey area and desired resolution increase. This exponential growth {\textendash} in combination with the increased complexity of the next-generation of iterative wave-equation based inversion algorithms {\textendash} puts strains on our acquisition systems and computational back ends impeding progress in our field. During this talk, I will review how recent randomized algorithms from Compressive Sensing and Machine learning can be used to overcome some of these challenges by fundamentally rethinking how we sample and process seismic data. The key idea here is to reduce acquisition and computational costs by deliberately working on small randomized subsets of the data at a desired accuracy. I will illustrate these concepts using a variety of compelling examples on realistic synthetics and field data.},
	keywords = {FWI, Imaging, randomization, seismic},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/herrmann2015IIPFWIras/herrmann2015IIPFWIras_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {kumar2015IIPFWIrmb,
	title = {Rank minimization based seismic data processing and inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present the low-rank extensions of full-waveform inversion to mitigate the local-minima issues. The purpose formulation is computationally tractable and does not involve any extra computational cost compared to the adjoint-state method.},
	keywords = {FWI, inversion, Processing, rank minimization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/kumar2015IIPFWIrmb/kumar2015IIPFWIrmb_pres.pdf},
	author = {Rajiv Kumar and Curt Da Silva and Oscar Lopez and Aleksandr Y. Aravkin and Hassan Mansour and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@conference {lopez2015EAGErma,
	title = {Rank minimization via alternating optimization: seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {Low-rank matrix completion techniques have recently become an effective tool for seismic trace interpolation problems. In this talk, we consider an alternating optimization scheme for nuclear norm minimization and discuss the applications to large scale wave field reconstruction. By adopting a factorization approach to the rank minimization problem we write our low-rank matrix in bi-linear form, and modify this workflow by alternating our optimization to handle a single matrix factor at a time. This allows for a more tractable procedure that can robustly handle large scale, highly oscillatory and critically subsampled seismic data sets. We demonstrate the potential of this approach with several numerical experiments on a seismic line from the Nelson 2D data set and a frequency slice from the Gulf of Mexico data set.},
	keywords = {EAGE, interpolation, low-rank, matrix completion},
	doi = {10.3997/2214-4609.201413453},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/lopez2015EAGErma/lopez2015EAGErma.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/lopez2015EAGErma/lopez2015EAGErma_poster.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADrdc,
	title = {Recent developments in compressive sensing for time-lapse studies},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present an overview of the advances we have made with respect to extending compressive sensing ideas to time-lapse studies. We show applications in compressive time-lapse seismic data acquisition, imaging and full-waveform inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/oghenekohwo2015SINBADrdc/oghenekohwo2015SINBADrdc.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Ning Tu and Felix J. Herrmann}
}
@presentation {dasilva2015SINBADrii,
	title = {Recent improvements to the 2/3{\textemdash}D imaging \& inversion algorithms in the SLIM software release},
	year = {2015},
	publisher = {SINBAD},
	abstract = {The SLIM software release contains algorithms for frequency domain 2D imaging and inversion based on direct LU and QR factorizations. Recent improvments include significant speedups, by utilizing the latest parallel direct factorization algorithms and inversion strategies which solve for each frequency on its corresponding grid. The iterative Helmholtz solvers available in the software release are now also applicable to 2D problems, extending the 2D algorithms to very large \& high frequency 2D imaging and inversion.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/dasilva2015SINBADrii/dasilva2015SINBADrii.pdf},
	author = {Curt Da Silva and Bas Peters and Felix J. Herrmann}
}
@techreport {peters2015EAGErwi,
	title = {Regularizing waveform inversion by projection onto intersections of convex sets},
	number = {TR-EOAS-2015-4},
	year = {2015},
	month = {01},
	institution = {UBC},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function, several gradient and quasi-Newton based solvers and does not require significant additional computation. The method is demonstrated on the inversion of very noisy synthetic data and vertical seismic profiling field data.},
	keywords = {convex sets, regularization, waveform inversion},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/peters2015EAGErwi/peters2015EAGErwi.html},
	author = {Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {peters2015SINBADrwi,
	title = {Regularizing waveform inversion by projections onto convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function and does not require significant additional computation. The method is demonstrated on the inversion of the 2D marine isotropic elastic synthetic seismic benchmark by Chevron using an acoustic modeling code. To highlight the effect of the projections, we apply no data pre-processing.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/peters2015SINBADrwi/peters2015SINBADrwi.pdf},
	author = {Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}
@techreport {peters2015SEGrwi,
	title = {Regularizing waveform inversion by projections onto convex sets {\textendash}- application to the 2D Chevron 2014 synthetic blind-test dataset},
	number = {TR-EOAS-2015-7},
	year = {2015},
	month = {06},
	institution = {UBC},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function and does not require significant additional computation. The method is demonstrated on the inversion of the 2D marine isotropic elastic synthetic seismic benchmark by Chevron using an acoustic modeling code. To highlight the effect of the projections, we apply no data pre-processing.},
	keywords = {blind-test, projection, regularization, SEG, Wavefield Reconstruction Inversion, waveform inversion},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2015/peters2015SEGrwi/peters2015SEGrwi.html},
	author = {Bas Peters and Zhilong Fang and Brendan R. Smithyman and Felix J. Herrmann}
}
@conference {louboutin2015IIPFWIrwi,
	title = {Regularizing waveform inversion by projections onto convex sets},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {A framework is proposed for regularizing the waveform inversion problem by projections onto intersections of convex sets. Multiple pieces of prior information about the geology are represented by multiple convex sets, for example limits on the velocity or minimum smoothness conditions on the model. The data-misfit is then minimized, such that the estimated model is always in the intersection of the convex sets. Therefore, it is clear what properties the estimated model will have at each iteration. This approach does not require any quadratic penalties to be used and thus avoids the known problems and limitations of those types of penalties. It is shown that by formulating waveform inversion as a constrained problem, regularization ideas such as Tikhonov regularization and gradient filtering can be incorporated into one framework. The algorithm is generally applicable, in the sense that it works with any (differentiable) objective function and does not require significant additional computation. The method is demonstrated on the inversion of the 2D marine isotropic elastic synthetic seismic benchmark by Chevron using an acoustic modeling code. To highlight the effect of the projections, we apply no data pre-processing. This is joint work with Bas Peters.},
	keywords = {convex, FWI, regularization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/louboutin2015IIPFWIrwi/louboutin2015IIPFWIrwi_pres.pdf},
	author = {Mathias Louboutin and Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {peters2015SINBADFrwi,
	title = {Regularizing waveform inversion by projections onto intersections of convex sets},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Common strategies to regularize waveform inversion (and other geophysical inverse problems) are adding quadratic penalty terms to the objective function or filtering the gradients used to update the model estimate. An example are penalties or filters to prevent/filter spurious high spatial frequency oscillations in the model while working with low frequency data. We present an alternative way of regularization, which works by projecting the model onto an intersection of convex sets, where each sets encodes certain desired model properties. This approach has certain theoretical and practical advantages over quadratic penalties or gradient filters. Some examples of useful convex sets in various challenging waveform inversion settings are shown on both real and synthetic data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/peters2015SINBADFrwi/peters2015SINBADFrwi.mov},
	author = {Bas Peters and Brendan R. Smithyman and Mathias Louboutin and Felix J. Herrmann}
}
@conference {esser2015CAMSAPrsa,
	title = {Resolving scaling ambiguities with the $\ell_1/\ell_2$ norm in a blind deconvolution problem with feedback},
	booktitle = {IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing},
	year = {2015},
	note = {(IEEE CAMSAP Workshop, Canc{\'u}n, Mexico)},
	month = {12},
	pages = {365-368},
	abstract = {Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with $\ell_1$ constraints. However, when paired with an $\ell_1/\ell_2$ constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal into positive and negative components and apply an $\ell_1/\ell_2$ constraint to the difference, thereby obtaining a constraint that is easy to implement. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {$\ell_1/\ell_2$, blind deconvolution, CAMSAP, lifting, method of multipliers},
	doi = {10.1109/CAMSAP.2015.7383812},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa_poster.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/CAMSAP/2015/esser2015CAMSAPrsa/esser2015CAMSAPrsa_ext.pdf},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {esser2015SINBADFrsa,
	title = {Resolving scaling ambiguities with the L1/L2 norm in a blind deconvolution problem with feedback},
	year = {2015},
	publisher = {SINBAD},
	abstract = {Compared to more mundane blind deconvolution problems, blind deconvolution in seismic applications involves a feedback mechanism related to the free surface. The presence of this feedback mechanism gives us an unique opportunity to remove ambiguities that have plagued blind deconvolution for a long time. While beneficial, this feedback by itself is insufficient to remove the ambiguities even with L1 constraints. However, when paired with an L1/L2 constraint the feedback allows us to resolve the scaling ambiguity under relatively mild assumptions. Inspired by lifting approaches, we propose to split the sparse signal into positive and negative components and apply an \ell_1/\ell_2 constraint to the difference, thereby obtaining a constraint that is easy to implement. Numerical experiments demonstrate robustness to the initialization as well as to noise in the data.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/esser2015SINBADFrsa/esser2015SINBADFrsa.mov},
	author = {Ernie Esser and Rongrong Wang and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2014EAGEWSrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {EAGE Workshop on Land and Ocean Bottom; Broadband Full Azimuth Seismic Surveys; Spain},
	year = {2014},
	note = {(EAGE Workshop, Spain)},
	month = {04},
	keywords = {4D seismic, EAGE, Marine acquisition, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/herrmann2014EAGEWSrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {wason2014SEGrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {46-51},
	abstract = {We present an extension of our time-jittered simultaneous marine acquisition to time-lapse surveys where the requirement for repeatability in acquisition can be waived provided we know the acquisition geometry afterwards. Our method, which does not require repetition, gives 4-D signals comparable to conventional methods where repeatability is key to their success.},
	keywords = {Acquisition, deblending, marine, SEG, time-laspe},
	doi = {http://dx.doi.org/10.1190/segam2014-1677.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/wason2014SEGrrt/wason2014SEGrrt_pres.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@presentation {wason2014SINBADrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will show how randomization techniques from (distributed) compressed sensing affect the way we think about time-lapse surveys.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/wason2014SINBADrrt/wason2014SINBADrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@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}
}
@presentation {oghenekohwo2014SINBADrsw,
	title = {Randomized sampling without repetition in time-lapse seismic surveys},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In this talk, we will show a method for acquiring time-lapse data, where we do not have to repeat the survey geometry. Our method works provided our acquisition is randomized, where sources and receivers are at random locations on a computational grid, and provided we know the spatial locations of the shots and receivers. In addition, we show the implications of either (sub)sampling more for the baseline and less for the monitor or vice-versa, and how this sampling scheme affects the 4-D signal.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/oghenekohwo2014SINBADrsw/oghenekohwo2014SINBADrsw.pdf},
	author = {Felix Oghenekohwo and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@conference {oghenekohwo2014SEGrsw,
	title = {Randomized sampling without repetition in time-lapse surveys},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2014},
	note = {(SEG)},
	month = {10},
	pages = {4848-4852},
	abstract = {Vouching for higher levels of repeatability in acquisition and processing of time-lapse (4D) seismic data has become the standard with oil and gas contractor companies, with significant investment in the design of acquisition systems and processing algorithms that attempt to address some of the current 4D challenges, in particular, imaging weak 4D signals. Recent developments from the field of compressive sensing have shown the benefits of variants of randomized sampling in marine seismic acquisition and its impact for the future of seismic exploration. Following these developments, we show that the requirement for accurate survey repetition in time-lapse seismic data acquisition can be waived provided we solve a sparsity-promoting convex optimization program that makes use of the shared component between the baseline and monitor data. By setting up a framework for inversion of the stacked sections of a time-lapse data, given the pre-stack data volumes, we are able to extract 4D signals with relatively highfidelity from significant subsamplings. Our formulation is applied to time-lapse data that has been acquired with different source/receiver geometries, paving the way for an efficient approach to dealing with time-lapse data acquired with initially poor repeatability levels, provided the survey geometry details are known afterwards.},
	keywords = {4D, Acquisition, random, repetition, SEG, time-lapse},
	doi = {http://dx.doi.org/10.1190/segam2014-1627.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/oghenekohwo2014SEGrsw/oghenekohwo2014SEGrsw_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {oghenekohwo2014SINBADrst,
	title = {Randomized subsampling in time-lapse surveys and recovery techniques},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk centres on new insights on how we think of acquisition of time-lapse data using ideas from compressed sensing. Specifically, we will show how simultaneous processing of time-lapse data from randomized subsampling gives more reliable estimates of the vintages and time-lapse signal, compared to parallel processing.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/oghenekohwo2014SINBADrst/oghenekohwo2014SINBADrst.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Haneet Wason and Ernie Esser and Felix J. Herrmann}
}
@presentation {lopez2014SINBADrma,
	title = {Rank minimization via alternating optimization},
	year = {2014},
	publisher = {SINBAD},
	abstract = {This talk discusses the benefits of an alternating optimization approach for seismic trace interpolation via rank minimization. We consider recent factorization approaches to rank minimization problems where we write our low rank matrix in bi-linear form, and modify this workflow by alternating our optimization to handle a single matrix factor at a time. This allows for a more tractable procedure that can better handle highly sub sampled data sets without increasing the time complexity. We demonstrate the potential of this approach with several seismic trace interpolation experiments.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/lopez2014SINBADrma/lopez2014SINBADrma.pdf},
	author = {Oscar Lopez and Rajiv Kumar and Felix J. Herrmann}
}
@conference {herrmann2014ROSErpe,
	title = {Relax the physics and expand the search space {\textendash} FWI via Wavefield Reconstruction Inversion},
	booktitle = {ROSE Consortium; Norway},
	year = {2014},
	note = {(ROSE Consortium)},
	month = {05},
	keywords = {Consortium, FWI, ROSE, WRI},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ROSE/2014/herrmann2014ROSErpe.pdf},
	author = {Felix J. Herrmann}
}
@presentation {herrmann2014SINBADrpe,
	title = {Relax the physics \& expand the search space {\textendash} FWI via Wavefield Reconstruction Inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {During this talk, we will present a new formulation of full-waveform inversion that combines the best of full-space constrained methods and reduced-space unconstrained methods. Instead of eliminating the constraint, which leads to the reduced adjoint-state method that underpins most formulations of full-waveform inversion, our method relaxes the PDE constraint by replacing it by an additive (least-squares) penalty term. By using the method of variable projection, we arrive at a formulation that alternates between solving for the wavefield, given the velocity model \& data, and solving for velocity-model updates, given the wavefields. We named this method Wavefield Reconstruction Inversion (WRI) because it inverts for the model updates by reconstructing the wavefield everywhere given data observed at the receivers and the physics of the wave equation. During the talk, we present this new method and discuss how the increased search space, now consisting of the wavefields and model, mitigates the effects of local minima. We will also discuss recent extensions including multi-parameter inversion and regularization. This is joint work with Tristan van Leeuwen, Bas Peters, and Ernie Esser.},
	keywords = {Presentation, SINBAD, SINBADSPRING2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Spring/herrmann2014SINBADrpe/herrmann2014SINBADrpe.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2013KAUSTrse,
	title = {Randomized sampling in exploration seismology},
	booktitle = {KAUST},
	year = {2013},
	month = {05},
	publisher = {KAUST},
	organization = {KAUST},
	keywords = {Exploration seismology, KAUST, randomized sampling},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/KAUST/2013/herrmann2013KAUSTrse/herrmann2013KAUSTrse_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {Herrmann13NIPSrse,
	title = {Randomized sampling in exploration seismology},
	booktitle = {NIPS},
	year = {2013},
	url = {http://techtalks.tv/talks/randomized-sampling-in-exploration-seismology/57871/},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2013/Herrmann13NIPSrse/Herrmann13NIPSrse.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2013SPIErse,
	title = {Randomized sampling in exploration seismology},
	booktitle = {SPIE Optics and Photonics: Wavelets and Sparsity XV},
	year = {2013},
	month = {08},
	keywords = {Exploration seismology, randomized sampling, SPIE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2013/herrmann2013SPIErse/herrmann2013SPIErse_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2013SEGOMANrdw,
	title = {Recent developments in wave-equation based inversion technology},
	booktitle = {SEG Workshop on FWI; Oman},
	year = {2013},
	month = {04},
	keywords = {3D, Exploration seismology, FWI, randomized sampling, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/herrmann2013SEGOMANrdw/herrmann2013SEGOMANrdw_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {kumar2013SEGHSS,
	title = {Reconstruction of seismic wavefields via low-rank matrix factorization in the hierarchical-separable matrix representation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {3628-3633},
	abstract = {Recent developments in matrix rank optimization have allowed for new computational approaches in the field of seismic data interpolation. In this paper, we propose an approach for seismic data interpolation which incorporates the Hierarchical Semi-Separable Structure (HSS) inside rank-regularized least-squares formulations for the missing-trace interpolation problem. The proposed approach is suitable for large scale problems, since it avoids SVD computations and uses a low-rank factorized formulation instead. We illustrate the advantages of the new HSS approach by interpolating a seismic line from the Gulf of Suez and compare the reconstruction with conventional rank minimization.},
	keywords = {HSS, interpolation, SEG},
	doi = {10.1190/segam2013-1165.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGHSS/kumar2013SEGHSS.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGHSS/kumar2013SEGHSS_pres.pdf},
	author = {Rajiv Kumar and Hassan Mansour and Aleksandr Y. Aravkin and Felix J. Herrmann}
}
@presentation {warner2013SINBADrfwi,
	title = {Reflection FWI with a poor starting model},
	year = {2013},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/warner2013SINBADrfwi/warner2013SINBADrfwi.pdf},
	author = {Mike Warner}
}
@presentation {vanleeuwen2013SINBADrpp,
	title = {Relaxing the physics: a penalty method for full-waveform inversion},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Computationally efficient method of solving partial differential equation (PDE)-constrained optimization problems that occur in (geophysical) inversion problems. The method takes measured data from a physical system as input and minimizes an objective function that depends on an unknown model for the physical parameters, fields, and additional nuisance parameters such as the source function. The invention consists of a minimization procedure involving a cost functional comprising of a data-misfit term and a penalty term that measures how accurately the fields satisfy the PDE. The method is composed of two alternating steps, namely the solution of a system of equations forming the discretization of the data-augmented PDE, and the solution of physical model parameters from the PDE itself given the field that solves the data-augmented system and an estimate for the sources. Compared to all-at-once approaches to PDE-constrained optimization, there is no need to update and store the fields for all sources leading to significant memory savings. As in the all-at-once approach, the proposed method explores a larger search space and is therefore less sensitive to initial estimates for the physical model parameters. Contrary to the reduced formulation, the proposed method does not require the solution of an adjoint PDE, effectively halving the number of PDE solves and memory requirement. As in the reduced formulation, fields can be computed independently and aggregated, possibly in parallel.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/vanleeuwen2013SINBADrpp/vanleeuwen2013SINBADrpp.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@article {lin2013GEOPrepsi,
	title = {Robust estimation of primaries by sparse inversion via one-norm minimization},
	journal = {Geophysics},
	volume = {78},
	number = {3},
	year = {2013},
	month = {05},
	pages = {R133-R150},
	abstract = {A recently proposed method called estimation of primaries by sparse inversion (EPSI) avoids the need for adaptive subtraction of approximate multiple predictions by directly inverting for the multiple-free subsurface impulse response as a collection of band-limited spikes. Although it can be shown that the correct primary impulse response is obtained through the sparsest possible solution, the original EPSI algorithm was not designed to take advantage of this result, and instead it relies on a multitude of inversion parameters, such as the level of sparsity per gradient update. We proposed and tested a new algorithm, named robust EPSI, in which we make obtaining the sparsest solution an explicit goal. Our approach remains a gradient-based approach like the original algorithm, but it is derived from a new biconvex optimization framework based on an extended basis-pursuit denoising formulation. Furthermore, because it is based on a general framework, robust EPSI can recover the impulse response in transform domains, such as sparsifying curvelet-based representations, without changing the underlying algorithm. We discovered that the sparsity-minimizing objective of our formulation enabled it to operate successfully on a variety of synthetic and field marine data sets without excessive tweaking of inversion parameters. We also found that recovering the solution in alternate sparsity domains can significantly improve the quality of the directly estimated primaries, especially for weaker late-arrival events. In addition, we found that robust EPSI produces a more artifact-free impulse response compared to the original algorithm.},
	keywords = {algorithm, biconvex, EPSI, multiples, Optimization, Pareto, sparsity, waveform inversion},
	doi = {10.1190/geo2012-0097.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2013/lin2013GEOPrepsi/lin2013GEOPrepsi.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@presentation {wason2012SINBADrma,
	title = {Randomized marine acquisition for ocean-bottom surveys},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/wason2012SINBADrma/wason2012SINBADrma_pres.pdf},
	author = {Haneet Wason}
}
@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}
}
@presentation {friedlander2012SINBADrsh,
	title = {Randomized sampling: How confident are you?},
	year = {2012},
	publisher = {SINBAD},
	abstract = {At last year{\textquoteright}s consortium meeting, I described an inexact gradient method and sampling scheme for data fitting. The randomization method has good convergence properties, at least as measured by the distance to the solution{\textendash}{\textendash}in expectation. But as one insightful critic rightly pointed out, we don{\textquoteright}t usually observe the expectation, at least not in a single run. In this talk I will characterize the convergence of the method in terms of bounds on the probability of being too far away from the solution.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/friedlander2012SINBADrsh/friedlander2012SINBADrsh_pres.pdf},
	author = {Michael P. Friedlander}
}
@presentation {dasilva2012SINBADrdp,
	title = {Recent developments in preconditioning the wave-equation Hessian},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Spring/dasilva2012SINBADrdp/dasilva2012SINBADrdp_pres.pdf},
	author = {Curt Da Silva}
}
@presentation {Lin2012SINBADrdr,
	title = {Recent developments on the robust estimation of primaries by sparse inversion},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Robust estimation of primaries by sparse inversion is a next-generation surface multiple removal technique with an objective to truly invert an operator that models the free-surface. Key to the success of this approach is the imposition of a sparsity constraint on the primary impulse response in the time domain. This is accomplished by carefully applying large-scale convex optimization techniques on an extended L1 minimization problem. One of the benefits of our approach is that many extensions to the algorithm can be devised under this optimization framework to improve the quality of the solution given fixed computational costs and mitigating various shortcomings in field data. This talk will first review the basic technique of Robust EPSI and follow with some highlights on recent further developments of the algorithm, including a discussion on the role of regularization by reciprocity and the interpolation of near-offset data, as well as investigations into optimality and robustness to data outliers.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/Lin2012SINBADrdr/Lin2012SINBADrdr_pres.pdf},
	author = {Tim T.Y. Lin}
}
@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}
}
@article {Aravkin11TRridr,
	title = {Robust inversion, dimensionality reduction, and randomized sampling},
	journal = {Mathematical Programming},
	volume = {134},
	number = {1},
	year = {2012},
	month = {08},
	pages = {101-125},
	abstract = {We consider a class of inverse problems in which the forward model is the solution operator to linear ODEs or PDEs. This class admits several dimensionality-reduction techniques based on data averaging or sampling, which are especially useful for large-scale problems. We survey these approaches and their connection to stochastic optimization. The data-averaging approach is only viable, however, for a least-squares misfit, which is sensitive to outliers in the data and artifacts unexplained by the forward model. This motivates us to propose a robust formulation based on the Student{\textquoteright}s t-distribution of the error. We demonstrate how the corresponding penalty function, together with the sampling approach, can obtain good results for a large-scale seismic inverse problem with 50 \% corrupted data.},
	keywords = {FWI, Inverse problems, Optimization, Robust estimation, Seismic inversion, Stochastic optimization},
	doi = {10.1007/s10107-012-0571-6},
	url = {http://www.springerlink.com/content/35rwr101h5736340/},
	url2 = {https://slim.gatech.edu/Publications/Public/Journals/MathematicalProgramming/aravkin2012MPrid/aravkin2012MPrid.pdf},
	author = {Aleksandr Y. Aravkin and Michael P. Friedlander and Felix J. Herrmann and Tristan van Leeuwen}
}
@conference {aravkin2012ICASSProbustb,
	title = {Robust inversion via semistochastic dimensionality reduction},
	booktitle = {ICASSP},
	year = {2012},
	pages = {5245-5248},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {We consider a class of inverse problems where it is possible to aggregate the results of multiple experiments. This class includes problems where the forward model is the solution operator to linear ODEs or PDEs. The tremendous size of such problems motivates the use dimensionality reduction (DR) techniques based on randomly mixing experiments. These techniques break down, however, when robust data-fitting formulations are used, which are essential in cases of missing data, unusually large errors, and systematic features in the data unexplained by the forward model. We survey robust methods within a statistical framework, and propose a sampling optimization approach that allows DR. The efficacy of the methods are demonstrated for a large-scale seismic inverse problem using the robust Student{\textquoteright}s t-distribution, where a useful synthetic velocity model is recovered in the extreme scenario of 60\% corrupted data. The sampling approach achieves this recovery using 20\% of the effort required by a direct robust approach.},
	keywords = {ICASSP},
	doi = {10.1109/ICASSP.2012.6289103},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2012/AravkinFriedlanderLeeuwen/AravkinFriedlanderLeeuwen.pdf},
	author = {Aleksandr Y. Aravkin and Michael P. Friedlander and Tristan van Leeuwen}
}
@presentation {aravkin2011SINBADrsp,
	title = {A randomized, sparsity promoting, Gauss-Newton algorithm for seismic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/aravkin2011SINBADrsp_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrsp/li2011SINBADrsp_pres.pdf},
	author = {Aleksandr Y. Aravkin and Xiang Li}
}
@presentation {dasilva2011SINBADrdp,
	title = {Recent developments in preconditioning the FWI Hessian},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/dasilva2011SINBADrdp/dasilva2011SINBADrdp_pres.pdf},
	author = {Curt Da Silva}
}
@presentation {lin2011SINBADrep,
	title = {Robust EPSI in a curvelet-like representation domain},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/lin2011SINBADrep/lin2011SINBADrep_pres.pdf},
	author = {Tim T.Y. Lin}
}
@conference {aravkin2011SEGrobust,
	title = {Robust full-waveform inversion using the Student{\textquoteright}s t-distribution},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {2669-2673},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Full-waveform inversion (FWI) is a computational procedure to extract medium parameters from seismic data. Robust methods for FWI are needed to overcome sensitivity to noise and in cases where modeling is particularly poor or far from the real data generating process. We survey previous robust methods from a statistical perspective, and use this perspective to derive a new robust method by assuming the random errors in our model arise from the Student{\textquoteright}s t-distribution. We show that in contrast to previous robust methods, the new method progres- sively down-weighs large outliers, effectively ignoring them once they are large enough. This suggests that the new method is more robust and suitable for situations with very poor data quality or modeling. Experiments show that the new method recovers as well or better than previous robust methods, and can recover models with quality comparable to standard methods on noise-free data when some of the data is completely corrupted, and even when a marine acquisition mask is entirely ignored in the modeling. The ability to ignore a marine acquisition mask via robust FWI methods offers an opportunity for stochastic optimization methods in marine acquisition.},
	keywords = {Full-waveform inversion, Optimization, SEG},
	doi = {10.1190/1.3627747},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/aravkin11SEGrobust/aravkin11SEGrobust.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {aravkin2011SINBADrfwi,
	title = {Robust FWI using Student{\textquoteright}s t \& robust source estimation},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/aravkin2011SINBADrfwi/aravkin2011SINBADrfwi_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@presentation {aravkin2011SINBADSPRINGrfwi,
	title = {Robust FWI using Student{\textquoteright}s t-distribution},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/aravkin2011SINBADSPRINGrfwi/aravkin2011SINBADSPRINGrfwi_pres.pdf},
	author = {Aleksandr Y. Aravkin}
}
@conference {aravkin2011ICIAMrfwiu,
	title = {Robust FWI using Student{\textquoteright}s t-distribution},
	booktitle = {ICIAM},
	year = {2011},
	month = {07},
	publisher = {ICIAM 2011},
	organization = {ICIAM 2011},
	abstract = {Iterative inversion algorithms require repeated simulation of 3D time-dependent acoustic, elastic, or electromagnetic wave fields, extending hundreds of wavelengths and hundreds of periods. Also, seismic data is rich in information at every representable scale. Thus simulation-driven optimization approaches to inversion impose great demands on simulator efficiency and accuracy. While computer hardware advances have been of critical importance in bringing inversion closer to practical application, algorithmic advances in simulator methodology have been equally important. Speakers in this two-part session will address a variety of numerical issues arising in the wave simulation, and in its application to inversion.},
	keywords = {Full-waveform inversion, ICIAM, Optimization, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/aravkin2011ICIAMrfwiu/aravkin2011ICIAMrfwiu.pdf},
	author = {Aleksandr Y. Aravkin and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {friedlander2011SINBADrir,
	title = {Robust inversion, data-fitting, and inexact gradient methods},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/friedlander2011SINBADrir/friedlander2011SINBADrir_pres.pdf},
	author = {Michael P. Friedlander}
}
@conference {lin2011SEGrssde,
	title = {Robust source signature deconvolution and the estimation of primaries by sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {4354-4359},
	publisher = {Dept. of Earth and Ocean Sciences, University of British Columbia},
	organization = {Dept. of Earth and Ocean Sciences, University of British Columbia},
	abstract = {The past few years had seen some concentrated interest on a particular wavefield-inversion approach to the popular SRME multiple removal technique called Estimation of Primaries by Sparse Inversion (EPSI). EPSI promises greatly improved tolerance to noise, missing data, edge effect, and other physi- cal phenomenon generally not described by the SRME relation (van Groenestijn and Verschuur, 2009a,b). It is based on the premise that it is possible to stably invert for both the primary impulse response and the source signature despite beforehand having no (or very limited) explicit knowledge of latter. The key to successful applications of EPSI, as shown in very recent works (Savels et al., 2010), is a robust way to reconstruct very sparse primary impulse response events as part of the inversion process. Based on the various successful demonstrations in literature, there is a very strong sense that EPSI will also play an important role in future developments of source sig- nature deconvolution and the general recovering of wavefield spectrum.},
	keywords = {deconvolution, Presentation, Processing, SEG, sparse inversion},
	doi = {10.1190/1.3628116},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/lin11SEGrssde/lin11SEGrssde.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/lin11SEGrssde/lin11SEGrssde_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {herrmann2010EAGErds,
	title = {Randomized dimensionality reduction for full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	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 continues to reveal fundamental shortcomings in our ability to handle the ever increasing problem size numerically. Two causes can be identified as the main culprits responsible for this barrier. 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 of our survey areas continues to increase. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this. In this paper, we address this situation by randomized dimensionality reduction, which we adapt from the field of compressive sensing. In this approach, we combine deliberate randomized subsampling with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. With this reduction, we compute Newton-like updates at the cost of roughly one gradient update for the fully-sampled wavefield.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErds/herrmann10EAGErds.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErds/herrmann10EAGErds_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39352},
	author = {Felix J. Herrmann and Xiang Li}
}
@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}
}
@article {herrmann2010GEOPrsg,
	title = {Randomized sampling and sparsity: getting more information from fewer samples},
	journal = {Geophysics},
	volume = {75},
	number = {6},
	year = {2010},
	month = {12},
	pages = {WB173-WB187},
	publisher = {SEG},
	abstract = {Many seismic exploration techniques rely on the collection of massive data volumes that are subsequently mined for information during processing. Although this approach has been extremely successful in the past, current efforts toward higher-resolution images in increasingly complicated regions of the earth continue to reveal fundamental shortcomings in our workflows. Chiefly among these is the so-called {\textquotedblleft}curse of dimensionality{\textquotedblright} exemplified by Nyquist{\textquoteright}s sampling criterion, which disproportionately strains current acquisition and processing systems as the size and desired resolution of our survey areas continue to increase. We offer an alternative sampling method leveraging recent insights from compressive sensing toward seismic acquisition and processing for data that are traditionally considered to be undersampled. The main outcome of this approach is a new technology where acquisition and processing related costs are no longer determined by overly stringent sampling criteria, such as Nyquist. At the heart of our approach lies randomized incoherent sampling that breaks subsampling related interferences by turning them into harmless noise, which we subsequently remove by promoting transform-domain sparsity. Now, costs no longer grow significantly with resolution and dimensionality of the survey area, but instead depend only on transform-domain sparsity. Our contribution is twofold. First, we demonstrate by means of carefully designed numerical experiments that compressive sensing can successfully be adapted to seismic exploration. Second, we show that accurate recovery can be accomplished for compressively sampled data volumes sizes that exceed the size of conventional transform-domain data volumes by only a small factor. Because compressive sensing combines transformation and encoding by a single linear encoding step, this technology is directly applicable to acquisition and to dimensionality reduction during processing. In either case, sampling, storage, and processing costs scale with transform-domain sparsity. We illustrate this principle by means of number of case studies.},
	keywords = {Acquisition, Compressive Sensing, data acquisition, geophysical techniques, Nyquist criterion, Optimization, sampling methods, seismology, SLIM},
	doi = {10.1190/1.3506147},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2010/herrmann2010GEOPrsg/herrmann2010GEOPrsg.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2010EAGErss,
	title = {Randomized sampling strategies},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	month = {06},
	abstract = {Seismic exploration relies on the collection of massive data volumes that are subsequently mined for information during seismic processing. While this approach has been extremely successful in the past, the current trend towards higher quality images in increasingly complicated regions continues to reveal fundamental shortcomings in our workflows for high-dimensional data volumes. Two causes 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 of our survey areas continues to increase. Secondly, there is the recent {\textquoteleft}{\textquoteleft}departure from Moore{\textquoteright}s law{\textquoteright}{\textquoteright} that forces us to lower our expectations to compute ourselves out of this curse of dimensionality. In this paper, we offer a way out of this situation by a deliberate randomized subsampling combined with structure-exploiting transform-domain sparsity promotion. Our approach is successful because it reduces the size of seismic data volumes without loss of information. As such we end up with a new technology where the costs of acquisition and processing are no longer dictated by the size of the acquisition but by the transform-domain sparsity of the end-product.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErss/herrmann10EAGErss.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/herrmann10EAGErss/herrmann10EAGErss_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=39131},
	author = {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}
}
@conference {lin2009DELPHIrwi,
	title = {Randomized wavefield inversion},
	booktitle = {DELPHI},
	year = {2009},
	note = {Presented at the DELPHI meeting. The Hague},
	keywords = {Presentation},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/Delphi/2009/lin2009DELPHIrwi/lin2009DELPHIrwi.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann and Yogi A. Erlangga}
}
@conference {herrmann2009SEGrpl,
	title = {Reflector-preserved lithological upscaling},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3466-3470},
	publisher = {SEG},
	organization = {SEG},
	abstract = {By combining Percolation models with lithological smoothing, we arrive at method for upscaling rock elastic constants that preserves reflections. In this approach, the Percolation model predicts sharp onsets in the elastic moduli of sand-shale mixtures when the shales reach a critical volume fraction. At that point, the shale inclusions form a connected cluster, and the macroscopic rock properties change with the power-law growth of the cluster. This switch-like nonlinearity preserves singularities, and hence reflections, even if no sharp transition exists in the lithology or if they are smoothed out using standard upscaling procedures.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255582},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGrpl/herrmann09SEGrpl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/herrmann09SEGrpl/herrmann09SEGrpl_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {wang2008SINBADrri,
	title = {Recent results in curvelet-based primary-multiple separation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {We present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploiting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/wang2008SINBADrri/wang2008SINBADrri.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@manual {hennenfent08MNrap,
	title = {Repro: a Python package for automating reproducible research in scientific computing},
	year = {2008},
	month = {08},
	abstract = {Repro is a Python package for automating reproducible research in scientific computing. Repro works in combination with SCons, a next-generation build tool. The package is freely available over the Internet. Downloading and installation instructions are provided in this gui de. The repro package is documented in various ways (many comments in source code, this guide{\textendash}-written using repro itself!{\textendash}-and a reference guide). In this user{\textquoteright}s guide, we present a few pedagogical examples that uses Matlab, Python, Seismic Unix (SU), and Madagascar. We also include demo pa pers. These papers are written in LaTeX and compiled using repro. The figures they contain are automatically generated from the source codes prov ided. In that sense, the demo papers are a model of self-contained documents that are fully reproducible. The repro package is largely inspired by some parts of Madagascar, a geophysical software package for reproducible research. However, the repro package is intended for a broad audience co ming from a wide spectrum of interest areas.},
	keywords = {SLIM},
	url = {http://repro.sourceforge.net/Site/Home.html},
	author = {Gilles Hennenfent and Sean Ross-Ross}
}
@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}
}
@conference {hennenfent2007SEGrsn,
	title = {Random sampling: new insights into the reconstruction of coarsely sampled wavefields},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2007},
	pages = {2575-2579},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this paper, we turn the interpolation problem of coarsely-sampled data into a denoising problem. From this point of view, we illustrate the benefit of random sampling at sub-Nyquist rate over regular sampling at the same rate. We show that, using nonlinear sparsity-promoting optimization, coarse random sampling may actually lead to significantly better wavefield reconstruction than equivalent regularly sampled data. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2793002},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/hennenfent07SEGrsn/hennenfent07SEGrsn_pres.pdf},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {herrmann2007EAGErdi,
	title = {Recent developments in curvelet-based seismic processing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2007},
	month = {06},
	abstract = {Combinations of parsimonious signal representations with nonlinear sparsity promoting programs hold the key to the next-generation of seismic data processing algorithms, since they allow for a formulation that is stable w.r.t. noise \& incomplete data do not require prior information on the velocity or locations and dips of the events},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2007/herrmann07EAGErdi/herrmann07EAGErdi_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=7548},
	author = {Felix J. Herrmann}
}
@conference {herrmann2007SINBADrdi2,
	title = {Recent developments in primary-multiple separation},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {In this talk, we present a novel primary-multiple separation scheme which makes use of the sparsity of both primaries and multiples in a transform domain, such as the curvelet transform, to provide estimates of each. The proposed algorithm utilizes seismic data as well as the output of a preliminary step that provides (possibly) erroneous predictions of the multiples. The algorithm separates the signal components, i.e., the primaries and multiples, by solving an optimization problem that assumes noisy input data and can be derived from a Bayesian perspective. More precisely, the optimization problem can be arrived at via an assumption of a weighted Laplacian distribution for the primary and multiple coefficients in the transform domain and of white Gaussian noise contaminating both the seismic data and the preliminary prediction of the multiples, which both serve as input to the algorithm. Time permitted, we will also briefly discuss a propasal for adaptive curvelet-domain matched filtering. This is joint work with Deli Wang, Rayan Saaba, {\o}zgur Yilmaz and Eric Verschuur.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2007/herrmann07SINBADrdi2/herrmann07SINBADrdi2_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2007SINBADrii,
	title = {Recent insights in $\ell_1$ solvers},
	booktitle = {SINBAD 2007},
	year = {2007},
	abstract = {During this talk, an overview is given on our work on norm-one solvers as part of the DNOISE project. Gilles will explain the ins and outs of our iterative thresholding solver based on log cooling while Felix will present the work of Michael Friedlander "A Newton root-finding algorithms for large-scale basis pursuit denoise". Both approaches involve the solution of the basis pursuit problem that seeks a minimum one-norm solution of an underdetermined least-squares problem. Basis pursuit denoise (BPDN) fits the least-squares problem only approximately, and a single parameter determines a curve that traces the trade-off between the least-squares fit and the one-norm of the solution. In the work of Friedlander, it is shown show that the function that describes this curve is convex and continuously differentiable over all points of interest. They describe an efficient procedure for evaluating this function and its derivatives. As a result, they can compute arbitrary points on this curve. Their method is suitable for large-scale problems. Only matrix-vector operations are required. This is joint work with Ewout van der Berg and Michael P. Friedlander},
	keywords = {Presentation, SINBAD, SLIM},
	author = {Gilles Hennenfent and Felix J. Herrmann}
}
@conference {wang2007SEGrri,
	title = {Recent results in curvelet-based primary-multiple separation: application to real data},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {26},
	year = {2007},
	pages = {2500-2504},
	publisher = {SEG},
	organization = {SEG},
	abstract = {In this abstract, we present a nonlinear curvelet-based sparsity-promoting formulation for the primary-multiple separation problem. We show that these coherent signal components can be separated robustly by explicitly exploting the locality of curvelets in phase space (space-spatial frequency plane) and their ability to compress data volumes that contain wavefronts. This work is an extension of earlier results and the presented algorithms are shown to be stable under noise and moderately erroneous multiple predictions. \copyright2007 Society of Exploration Geophysicists},
	keywords = {Presentation, SEG, SLIM},
	doi = {10.1190/1.2792986},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2007/wang07SEGrri/wang07SEGrri_pres.pdf},
	author = {Deli Wang and Rayan Saab and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {fomel2007ICASSPrepro,
	title = {Reproducible computational experiments using scons},
	booktitle = {ICASSP},
	year = {2007},
	publisher = {ICASSP},
	organization = {ICASSP},
	abstract = {SCons (from Software Construction) is a well-known open- source program designed primarily for building software. In this paper, we describe our method of extending SCons for managing data processing flows and reproducible computational experiments. We demonstrate our usage of SCons with a simple example.},
	keywords = {ICASSP},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICASSP/2007/fomel07ICASSPrepro/fomel07ICASSPrepro.pdf},
	author = {Sergey Fomel and Gilles Hennenfent}
}
@conference {hennenfent07gradsem,
	title = {Reproducible research in computational (geo)sciences},
	booktitle = {Graduate seminar series},
	year = {2007},
	month = {01},
	publisher = {Graduate Seminar Series},
	organization = {Graduate Seminar Series},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/Misc/hennenfent07gradsem.pdf},
	author = {Gilles Hennenfent}
}
@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 {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 {sastry2007SINBADrfu,
	title = {Recovery from unstructured data},
	booktitle = {SINBAD 2006},
	year = {2006},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/sastry2007SINBADrfu/sastry2007SINBADrfu.pdf},
	author = {Challa S. Sastry and Gilles Hennenfent and Felix J. Herrmann}
}
@conference {hennenfent2006SINBADros,
	title = {Recovery of seismic data: practical considerations},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {We propose a method for seismic data interpolation based on 1) the reformulation of the problem as a stable signal recovery problem and 2) the fact that seismic data is sparsely represented by curvelets. This method does not require information on the seismic velocities. Most importantly, this formulation potentially leads to an explicit recovery condition. We also propose a large-scale problem solver for the l1-regularization minimization involved in the recovery and successfully illustrate the performance of our algorithm on 2D synthetic and real examples.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADros/hennenfent2006SINBADros.pdf},
	author = {Gilles Hennenfent}
}
@conference {herrmann2005EAGErcd,
	title = {Robust curvelet-domain data continuation with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {A robust data interpolation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for solutions of wave equations with smooth coefficients. As such curvelets frames circum- vent {\textendash} besides the assumption of caustic-free data {\textendash} the necessity to make parametric assumptions (e.g. through linear/parabolic Radon or demigration) regarding the shape of events in seismic data. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data.},
	keywords = {EAGE, Presentation, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd/Herrmann05EAGErcd.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1112},
	author = {Felix J. Herrmann and Gilles Hennenfent}
}
@conference {herrmann2005EAGErcd1,
	title = {Robust curvelet-domain primary-multiple separation with sparseness constraints},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2005},
	month = {06},
	abstract = {A non-linear primary-multiple separation method using curvelets frames is presented. The advantage of this method is that curvelets arguably provide an optimal sparse representation for both primaries and multiples. As such curvelets frames are ideal candidates to separate primaries from multiples given inaccurate predictions for these two data components. The method derives its robustness regarding the presence of noise; errors in the prediction and missing data from the curvelet frame{\textquoteright}s ability (i) to represent both signal components with a limited number of multi-scale and directional basis functions; (ii) to separate the components on the basis of differences in location, orientation and scales and (iii) to minimize correlations between the coefficients of the two components. A brief sketch of the theory is provided as well as a number of examples on synthetic and real data.},
	keywords = {EAGE, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2005/Herrmann05EAGErcd1/Herrmann05EAGErcd1.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=1384},
	author = {Felix J. Herrmann and D. J. Verschuur}
}
