@conference {erdinc2025ML4SEISMICpbi,
	title = {Power-scaled Bayesian inference for velocity-model estimation},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Score-based generative models have emerged as powerful tools for conditional generation in Bayesian inference. While promising, a common criticism of these methods is their reliance on strong, structured priors, which can dominate the inference process and limit the network{\textquoteright}s dependence on observed data. To address this issue, we propose a modification to the generative sampling algorithm that incorporates power scaling of both priors and likelihoods. Our approach enables flexible control over the relative influence of the prior and likelihood without requiring retraining for different scaling configurations. We demonstrate this in the context of synthesizing seismic velocity models conditioned on imaged seismic data. By sampling from intermediate power posteriors, our method naturally supports sensitivity analysis and allows us to assess how varying prior and likelihood weights determines the behavior of the posterior. Through a comprehensive set of experiments, we examine the impact of the power parameters in three scenarios: scaling only the prior, scaling only the likelihood, and scaling both simultaneously. The results show that increasing the likelihood power up to a certain threshold improves the fidelity of posterior samples to the conditioning data, while reducing prior power increases structural diversity in the generated models. Furthermore, moderate likelihood scaling leads to reduced shot data residuals, demonstrating its effectiveness for posterior refinement.},
	keywords = {Bayesian inference, deep learning, generative model, Imaging, Inverse problems, ML4SEISMIC, power-scaling, RTM, score, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/erdinc2025ML4SEISMICpbi},
	author = {Huseyin Tuna Erdinc and Yunlin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {erdinc2025IMAGEpbi,
	title = {Power-scaled Bayesian inference with score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Bayesian inference, deep learning, generative model, IMAGE, Imaging, Inverse problems, power-scaling, RTM, score, SEG, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.48550/arXiv.2504.10807},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/erdinc2025IMAGEpbi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/erdinc2025IMAGEpbi/IMAGE_Tuna.pdf},
	author = {Huseyin Tuna Erdinc and Yunlin Zeng and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {henriksson2025ML4SEISMICpss,
	title = {Predicting Subsurface CO2 Saturation using Late Fusion of Seismic and Streaming Well Data},
	booktitle = {ML4SEISMIC Partners Meeting},
	year = {2025},
	month = {11},
	abstract = {Determining subsurface flow of CO2 in porous rock formations is a challenging task especially when it involves intergration of multimodal data that are collected at disparate timescales. In this work, we investigate how data fushion can be used to integrate multimodal data consisting on infrequently collected active-source seismic surveys and continuous streaming data of saturation measurements collected at monitoring wells. For the purpose, we propose a data fusion framework based on late fusion, which combines seismic images, computed from seismic surveys, with time-series data collected at the wells. By integrating the spatial information from seismic data with high-resolution but sparce spatial-temporal patterns from wells, we aim to better approximate the complex CO2 flow patterns.},
	keywords = {Amortized Variational Inference, Bayesian inference, conditional normalizing flows, data assimilation, deep learning, digital twin, GCS, Imaging, Inverse problems, late fusion, ML4SEISMIC, permeability, reservoir simulation, RTM, SLIM, Summary Statistics, Uncertainty quantification, WISE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2025/henriksson2025ML4SEISMICpss},
	author = {Victor Henriksson and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@unpublished {deng2025EAGEpjrm,
	title = {Probabilistic Joint Recovery Method for CO2 Plume Monitoring},
	year = {2025},
	month = {6},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, image generative model, Imaging, Inverse problems, JRM, Summary Statistics, Uncertainty quantification},
	doi = {10.48550/arXiv.2501.18761},
	url = {https://slim.gatech.edu/Publications/Public/Submitted/2025/deng2025EAGEpjrm/EAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@presentation {park2024ML4SEISMICpbi,
	title = {Physical Bayesian Inference for Two-Phase Flow Problems},
	year = {2024},
	month = {11},
	abstract = {Previous research on surrogate modeling of multiphase flow systems has shown that even models with low generalization error in forward predictions can generate posterior estimates that are out of distribution and physically unrealistic. To address this, we propose a regularization method that leverages the Fisher Information Matrix (FIM) to guide the training process. By integrating the FIM into a differentiable optimization framework, we aim to improve the reliability of surrogate models, such as Fourier Neural Operators (FNO), for both forward predictions and posterior inference. Our experiments on benchmark problems, including the Lorenz-63 system and Navier-Stokes equations, demonstrate that our approach significantly enhances physical consistency throughout time evolution, keeping predictions within the correct spatial distribution. Looking ahead, we plan to extend our framework to more complex applications, such as Geological Carbon Storage, with an emphasis on scaling FIM computations for high-dimensional problems.},
	keywords = {Bayesian inference, deep learning, Fisher Information Matrix, FNO, Inverse problems, ML4SEISMIC, SLIM, two-phase flow, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/park2024ML4SEISMICpbi},
	author = {Jeongjin Park and Huseyin Tuna Erdinc and Haoyun Li and Richard Rex and Nisha Chandramoorthy and Felix J. Herrmann}
}
@presentation {deng2024ML4SEISMICpjr,
	title = {Probabilistic Joint Recovery Method for CO2 plume monitoring},
	year = {2024},
	month = {11},
	abstract = {Accurately predicting fluid flow patterns in Carbon Capture and Storage (CCS) is a challenging task, particularly due to uncertainties in CO 2  plume dynamics and reservoir properties. While previous deterministic methods such as the Joint Recovery Method (JRM) have provided valuable insights, their effectiveness is limited as tools for decision-making since they do not communicate uncertainty. To address this, we propose a Probabilistic Joint Recovery Method (PJRM) that computes the posterior distribution at each monitoring survey while leveraging the shared structure among surveys through a common generative model. By efficiently computing posterior distributions for each monitoring survey, this method aims to provide valuable uncertainty information to decision-makers in CCS projects, augmenting their workflow with principled risk minimization.},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, igm, image generative model, Imaging, Inverse problems, JRM, ML4SEISMIC, SLIM, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2024/deng2024ML4SEISMICpjr},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {orozco2021NIPSpicp,
	title = {Photoacoustic Imaging with Conditional Priors from Normalizing Flows},
	booktitle = {Neural Information Processing Systems (NeurIPS)},
	year = {2021},
	note = {(NIPS, virtual)},
	month = {12},
	abstract = {Photoacoustic imaging is a biomedical imaging technique based on the photoacoustic effect. It leverages the interplay between optics and acoustics as a mean to circumvent the limitations of imaging modalities relying on single-type physics. Light beams generated by a pulsed laser can penetrate biological tissues by several centimeters, and are absorbed based on oxygen saturation or hemoglobin concentration. While optical absorption is in principle an ideal parameter for medical imaging (e.g., with respect to the detection of cancerous tissue), strong scattering imposes important limitations in its imaging resolution. Ultrasonics, on the other hand, can theoretically provide resolution of medical diagnostic value, but produce images of mechanical properties whose contrasts are not sensitive. In photoacoustics, optical and acoustic effects are combined to gain the best of both worlds. Under conditions of thermal and stress confinement, thermal energy can efficiently build up in biological tissues, which in turn undergo thermal expansion and effectively act as a spatially distributed acoustic source. In photoacoustic imaging, the actual object of interest is the induced source, as it is directly related to optical absorption and can be recovered with a relatively higher resolution than pure optical imaging, based on the acquired ultrasonic data.},
	keywords = {conditional prior, deep image, MAP, NIPS, normalizing flow, Photoacoustic, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2021/orozco2021NIPSpicp/deep_inverse_2021.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/NIPS/2021/orozco2021NIPSpicp/posterneurips2021_orozco.pdf},
	url2 = {https://openreview.net/forum?id=woi1OTvROO1},
	software = {https://github.com/slimgroup/InvertibleNetworks.jl},
	author = {Rafael Orozco and Ali Siahkoohi and Gabrio Rizzuti and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {zhang2021SEGapw,
	title = {A practical workflow for land seismic wavefield recovery with weighted matrix factorization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {145-149},
	abstract = {While wavefield reconstruction through weighted low-rank matrix factorizations has been shown to perform well on marine data, out-of-the-box application of this technology to land data is hampered by ground roll. The presence of these strong surface waves tends to dominate the reconstruction at the expense of the weaker body waves. Because ground roll is slow, it also suffers more from aliasing. To overcome these challenges, we introduce a practical workflow where the ground roll and body wave components are recovered separately and combined. We test the proposed approach blindly on a subset of the 3D SEAM Barrett dataset. With our technique, we recover densely sampled data from 25 percent randomly subsampled receivers. Independent comparisons on a single shot demonstrate significant improvements achievable with the presented workflow.},
	keywords = {3D SEAM Barrett dataset, ground roll, SEG, wavefield reconstruction, weighted matrix factorization},
	doi = {10.1190/segam2021-3594419.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/Yijun2021.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/zhang2021SEGapw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/zhang2021SEGapw/zhang2021SEGapw.mp4},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Yijun Zhang and Felix J. Herrmann}
}
@conference {siahkoohi2021AABIpto,
	title = {Preconditioned training of normalizing flows for variational inference in inverse problems},
	booktitle = {3rd Symposium on Advances in Approximate Bayesian Inference},
	year = {2021},
	note = {AABI},
	month = {01},
	abstract = {Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth. To meet these challenges, we propose a preconditioning scheme involving a conditional normalizing flow (NF) capable of sampling from a low-fidelity posterior distribution directly. This conditional NF is used to speed up the training of the high-fidelity objective involving minimization of the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density for indirect measurements at hand. To minimize costs associated with the forward operator, we initialize the high-fidelity NF with the weights of the pretrained low-fidelity NF, which is trained beforehand on available model and data pairs. Our numerical experiments, including a 2D toy and a seismic compressed sensing example, demonstrate that thanks to the preconditioning considerable speed-ups are achievable compared to training NFs from scratch.},
	keywords = {Inverse problems, Normalizing flows, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AABI/2021/siahkoohi2021AABIpto/siahkoohi2020ABIfab.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AABI/2021/siahkoohi2021AABIpto/siahkoohi2021AABIpto_pres.pdf},
	url2 = {https://openreview.net/forum?id=P9m1sMaNQ8T},
	software = {https://github.com/slimgroup/Software.siahkoohi2021AABIpto},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Mathias Louboutin and Philipp A. Witte and Felix J. Herrmann}
}
@conference {rizzuti2020SEGuqavp,
	title = {Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {1541-1545},
	abstract = {Uncertainty quantification for full-waveform inversion provides a probabilistic characterization of the ill-conditioning of the problem, comprising the sensitivity of the solution with respect to the starting model and data noise. This analysis allows to assess the confidence in the candidate solution and how it is reflected in the tasks that are typically performed after imaging (e.g., stratigraphic segmentation following reservoir characterization). Classically, uncertainty comes in the form of a probability distribution formulated from Bayesian principles, from which we seek to obtain samples. A popular solution involves Monte Carlo sampling. Here, we propose instead an approach characterized by training a deep network that "pushes forward" Gaussian random inputs into the model space (representing, for example, density or velocity) as if they were sampled from the actual posterior distribution. Such network is designed to solve a variational optimization problem based on the Kullback-Leibler divergence between the posterior and the network output distributions. This work is fundamentally rooted in recent developments for invertible networks. Special invertible architectures, besides being computational advantageous with respect to traditional networks, do also enable analytic computation of the output density function. Therefore, after training, these networks can be readily used as a new prior for a related inversion problem. This stands in stark contrast with Monte-Carlo methods, which only produce samples. We validate these ideas with an application to angle-versus-ray parameter analysis for reservoir characterization.},
	keywords = {Full-waveform inversion, SEG, Uncertainty quantification},
	doi = {10.1190/segam2020-3428150.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Gabrio Rizzuti and Ali Siahkoohi and Philipp A. Witte and Felix J. Herrmann}
}
@conference {herrmann2018SMCtpa,
	title = {The power of abstraction in Computational Exploration Seismology},
	booktitle = {Smoky Mountains Computational Sciences and Engineering Conference},
	year = {2018},
	month = {08},
	abstract = {The field of Computational Exploration Seismology has over the years benefited tremendously from developments in HPC. Back in the 80{\textquoteright}s and early 90{\textquoteright}s, the oil \& gas industry was among the main drivers of HPC technology with a resurgence about ten years ago with the advent of full-waveform inversion-i.e., an adjoint-state method for inverse problems that involve the wave equation. While the combination of hardware developments and tedious manual coding efforts have resulted in major improvements, the rate of innovations is slow in part because of the inherent complexities of the mostly monolithic code bases. As a result, innovations make it too slowly into these optimized codes. This stifles innovation and could prevent adaptation of important developments such as machine learning with deep convolutional neural networks into existing workflows. In comparison, the technological advances in the academic as well as commercial machine learning communities have been much faster in part because of robust and well abstracted code bases such as PyTorch and TensorFlow. Since both machine learning and adjoint-state methods rely on back propagation strong similarities exist ready to be exploited. During my talk, I will discuss lessons we learned in finding the appropriate abstractions, read Domain Specific Language, for time-stepping stencil-based finite differences in Devito, and how this is relevant for machine learning with deep networks. I will also share a vision on how ideas from machine learning can be merged into the development of the next-generation of wave-equation based imaging codes. In particular, I will demonstrate the remarkable ability of deep convolutional networks to map low-fidelity dispersed numerical wave simulations to high-fidelity ones. I feel that this apparently generalizable capability of neural nets opens a complete new approach to tackle fundamental problems in computational exploration seismology.},
	keywords = {SMC},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SMC/2018/herrmann2018SMCtpa/herrmann2018SMCtpa_pres.pdf},
	author = {Felix J. Herrmann and Gerard J. Gorman and Jan H{\"u}ckelheim and Keegan Lensink and Paul H. J. Kelly and Navjot Kukreja and Henryk Modzelewski and Michael Lange and Mathias Louboutin and Fabio Luporini and Ali Siahkoohi and Philipp A. Witte}
}
@article {peters2018pmf,
	title = {Projection methods and applications for seismic nonlinear inverse problems with multiple constraints},
	journal = {Geophysics},
	volume = {84},
	number = {2},
	year = {2018},
	note = {(Geophysics)},
	month = {02},
	pages = {R251-R269},
	abstract = {Nonlinear inverse problems are often hampered by non-uniqueness and local minima because of missing low frequencies and far offsets in the data, lack of access to good starting models, noise, and modeling errors. A well-known approach to counter these deficiencies is to include prior information on the unknown model, which regularizes the inverse problem. While conventional regularization methods have resulted in enormous progress in ill-posed (geophysical) inverse problems, challenges remain when the prior information consists of multiple pieces. To handle this situation, we propose an optimization framework that allows us to add multiple pieces of prior information in the form of constraints. Compared to additive regularization penalties, constraints have a number of advantages making them more suitable for inverse problems such as full-waveform inversion. The proposed framework is rigorous because it offers assurances that multiple constraints are imposed uniquely at each iteration, irrespective of the order in which they are invoked. To project onto the intersection of multiple sets uniquely, we employ Dykstra{\textquoteright}s algorithm that scales to large problems and does not rely on trade-off parameters. In that sense, our approach differs substantially from approaches such as Tikhonov regularization, penalty methods, and gradient filtering. None of these offer assurances, which makes them less suitable to full-waveform inversion where unrealistic intermediate results effectively derail the iterative inversion process. By working with intersections of sets, we keep expensive objective and gradient calculations unaltered, separate from projections, and we also avoid trade-off parameters. These features allow for easy integration into existing code bases. In addition to more predictable behavior, working with constraints also allows for heuristics where we built up the complexity of the model gradually by relaxing the constraints. This strategy helps to avoid convergence to local minima that represent unrealistic models. We illustrate this unique feature with examples of varying complexity.},
	keywords = {constraints, Full-waveform inversion, intersection, Optimization, projection, regularization},
	doi = {10.1190/geo2018-0192.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2018/peters2018pmf/peters2018pmf.html},
	author = {Bas Peters and Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {fang2017SINBADFpfg,
	title = {PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this work, we present a PDE-free Gauss-Newton Hessian for Wavefield Reconstruction Inversion. With this PDE-free Gauss-Newton Hessian, we can compute matrix-vector products without additional PDE solves. Thus, we are able to use the second order optimization method Gauss-Newton method with a roughly equal computational cost of first-order methods such as the gradient-descent method.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/fang2017SINBADFpfg/fang2017SINBADFpfg.mov},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@article {louboutin2016ppf,
	title = {Performance prediction of finite-difference solvers for different computer architectures},
	journal = {Computers \& Geosciences},
	volume = {105},
	year = {2017},
	note = {(Computers \& Geosciences)},
	month = {08},
	pages = {148-157},
	abstract = {The life-cycle of a partial differential equation (PDE) solver is often characterized by three development phases: the development of a stable numerical discretization; development of a correct (verified) implementation; and the optimization of the implementation for different computer architectures. Often it is only after significant time and effort has been invested that the performance bottlenecks of a PDE solver are fully understood, and the precise details varies between different computer architectures. One way to mitigate this issue is to establish a reliable performance model that allows a numerical analyst to make reliable predictions of how well a numerical method would perform on a given computer architecture, before embarking upon potentially long and expensive implementation and optimization phases. The availability of a reliable performance model also saves developer effort as it both informs the developer on what kind of optimisations are beneficial, and when the maximum expected performance has been reached and optimisation work should stop. We show how discretization of a wave-equation can be theoretically studied to understand the performance limitations of the method on modern computer architectures. We focus on the roofline model, now broadly used in the high-performance computing community, which considers the achievable performance in terms of the peak memory bandwidth and peak floating point performance of a computer with respect to algorithmic choices. A first principles analysis of operational intensity for key time-stepping finite-difference algorithms is presented. With this information available at the time of algorithm design, the expected performance on target computer systems can be used as a driver for algorithm design.},
	keywords = {finite differences, HPC, Modeling, performance},
	doi = {https://doi.org/10.1016/j.cageo.2017.04.014},
	url = {https://slim.gatech.edu/Publications/Public/Journals/ComputersAndGeosciences/2016/louboutin2016ppf/louboutin2016ppf.pdf},
	author = {Mathias Louboutin and Michael Lange and Felix J. Herrmann and Navjot Kukreja and Gerard Gorman}
}
@conference {peters2016SEGprs,
	title = {Parallel reformulation of the sequential adjoint-state method},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1411-1415},
	abstract = {This abstract is about reducing the total computation time to solve full-waveform inversion problems. A common problem formulation is a nonlinear least-squares problem where the gradient is computed using the adjoint-state algorithm. This formulation offers parallelism for the computation of gradients for different sources and frequencies. The adjoint-state algorithm itself is sequential however and this is a limiting factor when a lot of compute nodes are available and only a few wavefields need to be computed. This situation occurs when stochastic optimization strategies are used to minimize the objective function. We present a parallel reformulation of the sequential adjoint-state algorithm, which allows the forward- and adjoint wavefields to be computed in parallel. Both algorithms are mathematically equivalent but the parallel version is twice as fast in run time. An important characteristic of the proposed algorithm is that one wavefield needs to be computed per source and one per receiver. These fields can be used to apply the (inverse) Gauss-Newton Hessian to a vector without recomputing wavefields. A 2D example shows that good full-waveform inversion results are obtained, even when a small number of sources and receivers is used.},
	keywords = {FWI, numerical linear algebra, parallel computing, PDE-constrained optimization, SEG},
	doi = {10.1190/segam2016-13966771.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/peters2016SEGprs/peters2016SEGprs.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/peters2016SEGprs/peters2016SEGprs_pres.pdf},
	author = {Bas Peters and Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {witte2016SINBADFpve,
	title = {Phase velocity error minimizing scheme for the anisotropic pure P-wave equation},
	year = {2016},
	publisher = {SINBAD},
	abstract = {Pure P-wave equations for acoustic modeling in transverse isotropic media are derived by approximating the exact pure P-wave dispersion relation. In this work, we present an alternative to the common Taylor expansion based equations, in which we approximate the exact dispersion relation through a polynomial expansion and determine its coefficients by solving a least squares problem that minimizes the phase velocity error over the complete range of phase angles. We show that this approach is up to an order of magnitude more accurate than other pure P-wave equations at a comparable computational cost.},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/witte2016SINBADFpve/witte2016SINBADFpve.mov},
	author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}
@conference {witte2016SEGpve,
	title = {Phase velocity error minimizing scheme for the anisotropic pure P-wave equation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {452-457},
	abstract = {Pure P-wave equations for acoustic modeling in transverse isotropic media are derived by approximating the exact pure Pwave dispersion relation. In this work, we present an alternative approach to the approximate dispersion relation of Etgen and Brandsberg-Dahl, in which we approximate the exact dispersion relation through a polynomial expansion and determine its coefficients by solving a linear least squares problem that minimizes the phase velocity error over the entire range of phase angles. The coefficients are also optimized over a pre-defined range of Thomsen parameters, so that the phase error is small for models with spatially varying anisotropy. Phase velocity error analysis shows that the optimized pure P-wave equation is up to one order of magnitude more accurate than other popular pure P-wave equations, even for highly non-elliptic anisotropy. The optimized equation can be easily turned into a time-domain forward modeling scheme and comparisons of the modeled waveforms with analytical travel times once more illustrate its high accuracy. We also provide an efficient implementation of our approach for 3D tilted TI media that limits the count of fast Fourier transforms per time step to a number that is comparable to other pure P-wave equations.},
	keywords = {anisotropy, least squares, Modelling, SEG, TTI},
	doi = {10.1190/segam2016-13844850.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/witte2016SEGpve/witte2016SEGpve.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/witte2016SEGpve/witte2016SEGpve_poster.pdf},
	author = {Philipp A. Witte and Christiaan C. Stolk and Felix J. Herrmann}
}
@article {vanleeuwen2015IPpmp,
	title = {A penalty method for PDE-constrained optimization in inverse problems},
	journal = {Inverse Problems},
	volume = {32},
	number = {1},
	year = {2015},
	note = {(Inverse Problems)},
	month = {12},
	pages = {015007},
	abstract = {Many inverse and parameter estimation problems can be written as PDE-constrained optimization problems. The goal is to infer the parameters, typically coefficients of the PDE, from partial measurements of the solutions of the PDE for several right-hand sides. Such PDE-constrained problems can be solved by finding a stationary point of the Lagrangian, which entails simultaneously updating the parameters and the (adjoint) state variables. For large-scale problems, such an all-at-once approach is not feasible as it requires storing all the state variables. In this case one usually resorts to a reduced approach where the constraints are explicitly eliminated (at each iteration) by solving the PDEs. These two approaches, and variations thereof, are the main workhorses for solving PDE-constrained optimization problems arising from inverse problems. In this paper, we present an alternative method that aims to combine the advantages of both approaches. Our method is based on a quadratic penalty formulation of the constrained optimization problem. By eliminating the state variable, we develop an efficient algorithm that has roughly the same computational complexity as the conventional reduced approach while exploiting a larger search space. Numerical results show that this method indeed reduces some of the nonlinearity of the problem and is less sensitive to the initial iterate.},
	keywords = {Inverse problems, Optimization, PDE, penalty method},
	url = {https://slim.gatech.edu/Publications/Public/Journals/InverseProblems/2015/vanleeuwen2015IPpmp/vanleeuwen2015IPpmp.pdf},
	url2 = {http://stacks.iop.org/0266-5611/32/i=1/a=015007},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {bougher2015CSEGpsu,
	title = {Prediction of stratigraphic units from spectral co-occurance coefficients of well logs},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2015},
	note = {(CSEG, Calgary)},
	month = {05},
	abstract = {Well logging is the process of making physical measurements down bore holes in order to characterize geological and structural properties. Logs are visually interpreted and correlated to classify regions that are similar in structure, a process that can be modelled with machine learning. This project applies supervised learning methods to labelled well logs from the Trenton Black River data set in order to classify major stratigraphic units. Spectral co-occurance coefficients were used for feature extraction, and a k-nearest-neighbours approach was used for classification. This novel approach was applied to real field data in a high-impact domain, yielding promising results for future research.},
	keywords = {CSEG, machine learning, scattering transform, well logs},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/bougher2015CSEGpsu/bougher2015CSEGpsu.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2015/bougher2015CSEGpsu/bougher2015CSEGpsu_pres.pdf},
	author = {Ben B. Bougher and Felix J. Herrmann}
}
@mastersthesis {lin2015THpes,
	title = {Primary estimation with sparsity-promoting bi-convex optimization},
	year = {2015},
	note = {(PhD)},
	month = {10},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {This thesis establishes a novel inversion methodology for the surface-related primaries from a given recorded seismic wavefield, called the Robust Estimation of Primaries by Sparse Inversion (Robust EPSI, or REPSI). Surface-related multiples are a major source of coherent noise in seismic data, and inferring fine geological structures from active-source seismic recordings typically first necessitates its removal or mitigation. For this task, current practice calls for data-driven approaches which produce only approximate multiple models that must be non-linearly subtracted from the data, often distorting weak primary events in the process. A recently proposed method called Estimation of Primaries by Sparse Inversion (EPSI) avoids this adaptive subtraction by directly inverting for a discrete representation of the underlying multiple-free subsurface impulse response as a set of band-limited spikes. However, in its original form, the EPSI algorithm exhibits a few notable shortcomings that impede adoption. Although it was shown that the correct impulse response can be obtained through a sparsest solution criteria, the current EPSI algorithm is not designed to take advantage of this finding, but instead approximates a sparse solution in an ad-hoc manner that requires practitioners to decide on a multitude of inversion parameters. The Robust EPSI method introduced in this thesis reformulates the original EPSI problem as a formal bi-convex optimization problem that makes obtaining the sparsest solution an explicit goal, while also reliably admit satisfactory solutions using contemporary self-tuning gradient methods commonly seen in large-scale machine learning communities. I show that the Robust EPSI algorithm is able to operate successfully on a variety of datasets with minimal user input, while also producing a more accurate model of the subsurface impulse response when compared to the original algorithm. Furthermore, this thesis makes several contributions that improves the capability and practicality of EPSI: a novel scattering-based multiple prediction model that allows Robust EPSI to deal with wider near-offset receiver gaps than previously demonstrated for EPSI, as well as a multigrid-inspired continuation strategy that significantly reduces the computation time needed to solve EPSI-type problems. These additions are enabled by and built upon the formalism of the Robust EPSI as developed in this thesis.},
	keywords = {biconvex, EPSI, inversion, multiples, Optimization, PhD, sparsity},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/lin2015THpes/lin2015THpes.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2015/lin2015THpes/lin2015THpes_pres.pdf},
	author = {Tim T.Y. Lin}
}
@conference {herrmann2015SIAMpcf,
	title = {Pros and cons of full- and reduced-space methods for Wavefield Reconstruction Inversion},
	booktitle = {SIAM Conference on Mathematical and Computational Issues in the Geosciences},
	year = {2015},
	note = {(SIAM Conference on Mathematical and Computational Issues in the Geosciences, Stanford University, California)},
	month = {06-07},
	abstract = {By insisting on fitting observed data, Wavefield Reconstruction Inversion (WRI) is no longer cycle skipped and therefore less reliant on the accuracy of starting models. While extending the search space mitigates local minima, there are challenges scaling to 3D seismic when using reduced-space methods that require accurate solves. Conversely, full-space methods allow for inaccurate solves but require storage of all wavefields. We weigh pros and cons of these two approaches in the seismic context.},
	keywords = {FWI, SIAM, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2015/herrmann2015SIAMpcf/herrmann2015SIAMpcf_pres.pdf},
	author = {Felix J. Herrmann and Bas Peters}
}
@techreport {herrmann2014pmpde,
	title = {A penalty method for PDE-constrained optimization},
	number = {WO 2014/172787},
	year = {2014},
	note = {(International publication date 30 October 2014. International publication number WO 2014/172787.)},
	month = {10},
	institution = {UBC},
	type = {Patent},
	abstract = {The invention is directed to a computer-implemented method for obtaining a physical model having physical model parameters wherein solutions to one or more partial-differential-equations (PDE{\textquoteright}s) are calculated ans wherein (i) an appropriate initial model is selected, (ii) setup a system of equations referred to as the data-augmented PDE for the field, comprising of the discretized PDE, the sampling operator, the source function and the observed data, and solve the data-augmented PDE in a suitable manner to obtain a field that both satisfies the PDE and fits the data to some degree, (iii) setup a system of equations by using the PDE, the source function and the field obtained in step (ii) and solve this system of equations in a suitable manner to obtain an update of the physical model parameters and repeat steps (ii)-(iii) until a predetermined stopping criterion is met.},
	keywords = {Optimization, patent, penalty method},
	url = {https://slim.gatech.edu/Publications/Public/Patents/2014/herrmann2014pmpde/herrmann2014pmpde_WO2014172787.pdf},
	url2 = {http://patentscope.wipo.int/search/en/WO2014172787},
	author = {Felix J. Herrmann and Tristan van Leeuwen}
}
@presentation {fang2013SINBADp3dfwi,
	title = {Parallel 3D FWI with simultaneous shots},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In this work, we manage to build the workflow for the parallel 3D full-waveform inversion. In the forward simulation part, we generate the Helmholtz matrix in parallel and use parallel CARPCG to solve the Helmholtz equation. In the inversion process, simultaneous shots are used to reduce the computational costs. Additionally, we propose a method to select the number of simultaneous shots and tolerance of CARPCG dynamically, to reach a compromise between the computational costs and accuracy.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/fang2013SINBADp3dfwi/fang2013SINBADp3dfwi.pdf},
	author = {Zhilong Fang and Tristan van Leeuwen and Felix J. Herrmann}
}
@techreport {vanLeeuwen2013Penalty2,
	title = {A penalty method for PDE-constrained optimization},
	number = {TR-EOAS-2013-6},
	year = {2013},
	month = {04},
	institution = {UBC},
	abstract = {We present a method for solving PDE constrained optimization problems based on a penalty formulation. This method aims to combine advantages of both full-space and reduced methods by exploiting a large search-space (consisting of both control and state variables) while allowing for an efficient implementation that avoids storing and updating the state-variables. This leads to a method that has roughly the same per-iteration complexity as conventional reduced approaches while dening an objective that is less non-linear in the control variable by implicitly relaxing the constraint. We apply the method to a seismic inverse problem where it leads to a particularly ecient implementation when compared to a conventional reduced approach as it avoids the use of adjoint state-variables. Numerical examples illustrate the approach and suggest that the proposed formulation can indeed mitigate some of the well-known problems with local minima in the seismic inverse problem.},
	keywords = {Optimization, waveform inversion},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/vanLeeuwen2013Penalty2/vanLeeuwen2013Penalty2.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2013SEGpmc,
	title = {A penalty method for PDE-constrained optimization with applications to wave-equation based seismic inversion},
	booktitle = {SEG Workshop on Computational Mathematics for Geophysics; Houston},
	year = {2013},
	month = {09},
	keywords = {Optimization, penalty method, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/herrmann2013SEGpmc/herrmann2013SEGpmc_pres.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {smithyman2013SINBADprb,
	title = {Phase-residual based quality-control methods and techniques for mitigating cycle skips},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Most full-waveform inversion algorithms use local optimization methods to iteratively improve the numerical earth model. All of these make the implicit assumption that the the model close to the true earth to avoid cycle. In practice this may not be true. We explore two questions: 1. How do we understand and visualize the cycle skip phenomenon in order to recognize it if it exists? 2. How do we automate this quality control step and use the rich information from multiple data to mitigate cycle skips and avoid local minima?},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/smithyman2013SINBADprb/smithyman2013SINBADprb.pdf},
	author = {Brendan R. Smithyman and Felix J. Herrmann}
}
@presentation {pong2013SINBADppg,
	title = {The proximal-proximal gradient algorithm},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In many applications, one has to minimize the sum of a smooth loss function modeling misfit and a regularization term inducing structures. In this talk, we consider the case when the regularization is a composition of a convex function, whose proximal mapping is easy to compute, and a nonzero linear map. Such instances arise in system identification and realization problems. In this talk, we present a new algorithm, the proximal-proximal gradient algorithm, which admits easy subproblems. Our algorithm reduces to the proximal gradient algorithm if the linear map is just the identity map, and can be viewed as "very inexact" inexact proximal gradient algorithm. We show that the whole sequence generated from the algorithm converges to an optimal solution, and establish an upper bound on iteration complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf},
	author = {Ting Kei Pong and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {nutini2013SINBADpcb,
	title = {Putting the curvature back into sparse solvers},
	year = {2013},
	publisher = {SINBAD},
	abstract = {For many problems in signal and image processing, we seek a sparse solution that solves that approximately solves the problem Ax $\approx$ b, where A is an m-by-n matrix and b is an m-vector. Many of the most used approaches to problem thissuch as iterative soft thresholding SPGL1 andare first-order methods. As a result, these methods can sometimes be slow to converge. In this talk, we present an approach that takes advantage of the easily-obtainable second-order information. By exploiting this available second-order information, we are able to put the curvature back into sparse solvers and improve upon the convergence rates of existing solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/nutini2013SINBADpcb/nutini2013SINBADpcb.pdf},
	author = {Julie Nutini and Michael P. Friedlander and Felix J. Herrmann}
}
@techreport {vanleeuwen2012smii,
	title = {A parallel matrix-free framework for frequency-domain seismic modelling, imaging and inversion in Matlab},
	number = {TR-EOAS-2012-5},
	year = {2012},
	month = {07},
	abstract = {I present a parallel matrix-free framework for frequency-domain seismic modeling, imaging and inversion. The framework provides basic building blocks for designing and testing optimization-based formulations of both linear and non-linear seismic in- verse problems. By overloading standard linear-algebra operations, such as matrix-vector multiplications, standard optimization packages can be used to work with the code without any modification. This leads to a scalable testbed on which new methods can be rapidly prototyped and tested on medium-sized 2D problems. I present some numerical examples on both linear and non-linear seismic inverse problems.},
	keywords = {Matlab, object-oriented programming, Optimization, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2012/vanleeuwen2012smii/vanleeuwen2012smii.pdf},
	author = {Tristan van Leeuwen}
}
@techreport {vanleeuwen2012SEGparallel,
	title = {A parallel, object-oriented framework for frequency-domain wavefield imaging and inversion.},
	number = {TR-EOAS-2012-2},
	year = {2012},
	month = {04},
	institution = {Department of Earth and Ocean Sciences},
	address = {University of British Columbia, Vancouver},
	abstract = {We present a parallel object-oriented matrix-free framework for frequency-domain seismic modeling, imaging and inversion. The key aspects of the framework are its modularity and level of abstraction, which allows us to write code that reflects the underlying mathematical structure and develop unit-tests that guarantee the fidelity of the code. By overloading standard linear-algebra operations, such as matrix-vector multiplications, we can use standard optimization packages to work with our code without any modification. This leads to a scalable testbed on which new methods can be rapidly prototyped and tested on medium-sized 2D problems. Although our current implementation uses (parallel) Matlab, all of these design principles can also be met by using lower-level languages which is important when we want to scale to realistic 3D problems. We present some numerical examples on synthetic data.},
	keywords = {Imaging, inversion, Modeling, SEG},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2012/vanleeuwen2012SEGparallel/vanleeuwen2012SEGparallel.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {herrmann2012EAGEpmr,
	title = {Pass on the message: recent insights in large-scale sparse recovery},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Data collection, data processing, and imaging in exploration seismology increasingly hinge on large-scale sparsity promoting solvers to remove artifacts caused by efforts to reduce costs. We show how the inclusion of a "message term" in the calculation of the residuals improves the convergence of these iterative solvers by breaking correlations that develop between the model iterate and the linear system that needs to be inverted. We compare this message-passing scheme to state-of-the-art solvers for problems in missing-trace interpolation and in dimensionality-reduced imaging with phase encoding.},
	keywords = {EAGE, message passing, sparse inversion},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/herrmann2012EAGEpmr/herrmann2012EAGEpmr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/herrmann2012EAGEpmr/herrmann2012EAGEpmr_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=58935},
	author = {Felix J. Herrmann}
}
@conference {vanleeuwen2012EAGEcarpcg,
	title = {Preconditioning the Helmholtz equation via row-projections},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {3D frequency-domain full waveform inversion relies on being able to efficiently solve the 3D Helmholtz equation. Iterative methods require sophisticated preconditioners because the Helmholtz matrix is typically indefinite. We review a preconditioning technique that is based on row-projections. Notable advantages of this preconditioner over existing ones are that it has low algorithmic complexity, is easily parallelizable and extendable to time-harmonic vector equations.},
	keywords = {EAGE, Helmholtz equation, precondition},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanleeuwen2012EAGEcarpcg/vanleeuwen2012EAGEcarpcg.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanleeuwen2012EAGEcarpcg/vanleeuwen2012EAGEcarpcg_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=58891},
	author = {Tristan van Leeuwen and Dan Gordon and Rachel Gordon and Felix J. Herrmann}
}
@presentation {min2011SINBADpss,
	title = {Parameter-selection strategy for density in frequency-domain elastic waveform inversion},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/min2011SINBADpss/min2011SINBADpss_pres.pdf},
	author = {Dong-Joo Min}
}
@conference {vanleeuwen2011SEGext,
	title = {Probing the extended image volume},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {30},
	year = {2011},
	month = {09},
	pages = {4045-4050},
	publisher = {SEG},
	organization = {SEG},
	abstract = {The prestack image volume can be defined as a cross- correlation of the source and receivers wavefields for non-zero space and time lags. If the background velocity is kinemati- cally acceptable, this image volume will have its main contributions at zero lag, even for complex models. Thus, it is an ideal tool for wave-equation migration velocity analysis in the presence of strong lateral heterogeneity. In particular, it allows us to pose migration velocity analysis as a PDE-constrained optimization problem, where the goal is to minimize the energy in the image volume at non-zero lag subject to fitting the data approximately. However, it is computationally infeasi- ble to explicitly form the whole image volume. In this paper, we discuss several ways to reduce the computational costs involved in computing the image volume and evaluating the focusing criterion. We reduce the costs for calculating the data by randomized source synthesis. We also present an efficient way to subsample the image volume. Finally, we propose an alternative optimization criterion and suggest a multiscale inversion strategy for wave-equation MVA.},
	keywords = {Imaging, SEG},
	doi = {10.1190/1.3628051},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2011/vanleeuwen11SEGext/vanleeuwen11SEGext.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {vanleeuwen2011SINBADpei,
	title = {Probing the extended image volume},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADSPRING2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Spring/vanleeuwen2011SINBADpei/vanleeuwen2011SINBADpei_pres.pdf},
	author = {Tristan van Leeuwen}
}
@conference {vanleeuwen2011WAVESpeiv,
	title = {Probing the extended image volume for seismic velocity inversion},
	booktitle = {WAVES},
	year = {2011},
	month = {07},
	publisher = {Waves 2011},
	organization = {Waves 2011},
	abstract = {In seismic velocity inversion one aims to reconstruct a kinematically correct subsurface velocity model that can be used as input for further processing and inversion of the data. An important tool in velocity inversion is the prestack image volume. This image volume can be defined as a cross-correlation of the source and receivers wavefields for non-zero space and time lags. If the background velocity is kinematically acceptable, this image volume will have its main contributions at zero lag, even for complex models. Thus, it is an ideal tool for wave-equation migration velocity analysis in the presence of strong lateral heterogeneity. In particular, it allows us to pose migration velocity analysis as a PDE-constrained optimization problem, where the goal is to minimize the energy in the image volume at non-zero lag subject to fitting the data approximately. However, it is computationally infeasible to explicitly form the whole image volume. In this paper, we discuss several ways to reduce the computational costs involved in computing the image volume and evaluating the focusing criterion. We reduce the costs for calculating the data by randomized source synthesis. We also present an efficient way to subsample the image volume. Finally, we propose an alternative optimization criterion and suggest a multiscale inversion strategy for wave-equation MVA.},
	keywords = {Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ICIAM/2011/vanleeuwen2011WAVESpeiv/vanleeuwen2011WAVESpeiv.pdf},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {kumar2010SINBADpoe,
	title = {Parallelizing operations with ease using Parallel SPOT},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/kumar2010SINBADpoe/kumar2010SINBADpoe_pres.pdf},
	author = {Nameet Kumar}
}
@conference {herrmann2008SINBADpsm,
	title = {Phase-space matched filtering and migration preconditioning},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {During this talk, I will report on new phase-space regularization functionals defined in terms of splines. This spline representation reduces the dimensionality of estimating our phase-space matched filter. We will discuss how this filter can be used in migration preconditioning. This is joint work with Christiaan Stolk.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/herrmann2008SINBADpsm/herrmann2008SINBADpsm.pdf},
	author = {Felix J. Herrmann}
}
@article {berg2008SJSCpareto,
	title = {Probing the Pareto frontier for basis pursuit solutions},
	journal = {SIAM Journal on Scientific Computing},
	volume = {31},
	number = {2},
	year = {2008},
	month = {01},
	pages = {890-912},
	publisher = {SIAM},
	abstract = {The basis pursuit problem 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 optimal trade-off between the least-squares fit and the one-norm of the solution. We prove that this curve is convex and continuously differentiable over all points of interest, and show that it gives an explicit relationship to two other optimization problems closely related to BPDN. We describe a root-finding algorithm for finding arbitrary points on this curve; the algorithm is suitable for problems that are large scale and for those that are in the complex domain. At each iteration, a spectral gradient-projection method approximately minimizes a least-squares problem with an explicit one-norm constraint. Only matrix-vector operations are required. The primal-dual solution of this problem gives function and derivative information needed for the root-finding method. Numerical experiments on a comprehensive set of test problems demonstrate that the method scales well to large problems.},
	keywords = {basis pursuit, convex program, duality, Newton{\textquoteright}s method, one-norm regularization, Optimization, projected gradient, root-finding, sparse solutions},
	doi = {10.1137/080714488},
	url = {https://slim.gatech.edu/Publications/Public/Journals/SIAMJournalOnScientificComputing/2008/vanderberg08SIAMptp/vanderberg08SIAMptp.pdf},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@conference {herrmann2007COIPpti,
	title = {Phase transitions in explorations seismology: statistical mechanics meets information theory},
	booktitle = {COIP},
	year = {2007},
	abstract = {In this paper, two different applications of phase transitions to exploration seismology will be discussed. The first application concerns a phase diagram ruling the recovery conditions for seismic data volumes from incomplete and noisy data while the second phase transition describes the behavior of bi-compositional mixtures as a function of the volume fraction. In both cases, the phase transitions are the result of randomness in large system of equations in combination with nonlinearity. The seismic recovery problem from incomplete data involves the inversion of a rectangular matrix. Recent results from the field of "compressive sensing" provide the conditions for a successful recovery of functions that are sparse in some basis (wavelet) or frame (curvelet) representation, by means of a sparsity ($\ell_1$-norm) promoting nonlinear program. The conditions for a successful recovery depend on a certain randomness of the matrix and on two parameters that express the matrix{\textquoteright} aspect ratio and the ratio of the number of nonzero entries in the coefficient vector for the sparse signal representation over the number of measurements. It appears that the ensemble average for the success rate for the recovery of the sparse transformed data vector by a nonlinear sparsity promoting program, can be described by a phase transition, demarcating the regions for the two ratios for which recovery of the sparse entries is likely to be successful or likely to fail. Consistent with other phase transition phenomena, the larger the system the sharper the transition. The randomness in this example is related to the construction of the matrix, which for the recovery of spike trains corresponds to the randomly restricted Fourier matrix. It is shown, that these ideas can be extended to the curvelet recovery by sparsity-promoting inversion (CRSI). The second application of phase transitions in exploration seismology concerns the upscaling problem. To counter the intrinsic smoothing of singularities by conventional equivalent medium upscaling theory, a percolation-based nonlinear switch model is proposed. In this model, the transport properties of bi-compositional mixture models for rocks undergo a sudden change in the macroscopic transport properties as soon as the volume fraction of the stronger material reaches a critical point. At this critical point, the stronger material forms a connected cluster, which leads to the creation of a cusp-like singularity in the elastic moduli, which in turn give rise to specular reflections. In this model, the reflectivity is no longer explicitly due to singularities in the rocks composition. Instead, singularities are created whenever the volume fraction exceeds the critical point. We will show that this concept can be used for a singularity-preserved lithological upscaling.},
	keywords = {Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/COIP/2007/herrmann07COIPpti/herrmann07COIPpti_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {thomson2006SEGpwfd,
	title = {A parallel windowed fast discrete curvelet transform applied to seismic processing},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {25},
	year = {2006},
	pages = {2767-2771},
	publisher = {SEG},
	organization = {SEG},
	abstract = {We propose using overlapping, tapered windows to process seismic data in parallel. This method consists of numerically tight linear oper ators and adjoints that are suitable for use in iterative algorithms. This method is also highly scalable and makes parallel processing of large seismic data sets feasible. We use this scheme to define the Parallel Windowed Fast Discrete Curvelet Transform (PWFDCT), which we apply to a seismic data interpolation algorithm. The successful performance of our parallel processing scheme and algorithm on a two-dimensional synthetic data is shown.},
	keywords = {SEG},
	doi = {10.1190/1.2370099},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2006/thomson06SEGpwfd/thomson06SEGpwfd.pdf},
	author = {Darren Thomson and Gilles Hennenfent and Henryk Modzelewski and Felix J. Herrmann}
}
@conference {herrmann2006SINBADpms,
	title = {Primary-multiple separation by curvelet frames},
	booktitle = {SINBAD 2006},
	volume = {170},
	year = {2006},
	pages = {781-799},
	publisher = {Geophysical Journal International},
	organization = {Geophysical Journal International},
	abstract = {Predictive multiple suppression methods consist of two main steps: a prediction step, during which multiples are predicted from seismic data, and a primary-multiple separation step, during which the predicted multiples are {\textquoteright}matched{\textquoteright} with the true multiples in the data and subsequently removed. The last step is crucial in practice: an incorrect separation will cause residual multiple energy in the result or may lead to a distortion of the primaries, or both. To reduce these adverse effects, a new transformed-domain method is proposed where primaries and multiples are separated rather than matched. This separation is carried out on the basis of differences in the multiscale and multidirectional characteristics of these two signal components. Our method uses the curvelet transform, which maps multidimensional data volumes into almost orthogonal localized multidimensional prototype waveforms that vary in directional and spatio-temporal content. Primaries-only and multiples-only signal components are recovered from the total data volume by a nonlinear optimization scheme that is stable under noisy input data. During the optimization, the two signal components are separated by enhancing sparseness (through weighted l1-norms) in the transformed domain subject to fitting the observed data as the sum of the separated components to within a user-defined tolerance level. Whenever the prediction for the two signal components in the transformed domain correlate, the recovery is suppressed while for regions where the correlation is small the method seeks the sparsest set of coefficients that represent each signal component. Our algorithm does not seek a matched filter and as such it differs fundamentally from traditional adaptive subtraction methods. The method derives its stability from the sparseness obtained by a non-parametric multiscale and multidirectional overcomplete signal representation. This sparsity serves as prior information and allows for a Bayesian interpretation of our method during which the log-likelihood function is minimized while the two signal components are assumed to be given by a superposition of prototype waveforms, drawn independently from a probability function that is weighted by the predicted primaries and multiples. In this paper, the predictions are based on the data-driven surface-related multiple elimination (SRME) method. Synthetic and field data examples show a clean separation leading to a considerable improvement in multiple suppression compared to the conventional method of adaptive matched filtering. This improved separation translates into an improved stack.},
	keywords = {Presentation, SINBAD, SLIM},
	doi = {10.1111/j.1365-246X.2007.03360.x},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADpms/herrmann2006SINBADpms.pdf},
	author = {Felix J. Herrmann and Urs Boeniger and D. J. Verschuur}
}
@conference {herrmann2006SINBADapo1,
	title = {A primer on sparsity transforms: curvelets and wave atoms},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an overview will be given on the different sparsity transforms that are used at SLIM. Emphasis will be on two directional and multiscale wavelet transforms, namely the curvelet and the recently introduced wave-atom transforms. The main properties of these transforms will be listed and their performance on seismic data will be discussed.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADapo/hennenfent2006SINBADapo.pdf},
	author = {Felix J. Herrmann}
}
@conference {hennenfent2006SINBADapo,
	title = {A primer on sparsity transforms: curvelets and wave atoms},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an introduction will be given on the method of stable recovery from noisy and incomplete data. Strong recovery conditions that guarantee the recovery for arbitrary acquisition geometries will be reviewed and numerical recovery examples will be presented.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent2006SINBADapo/hennenfent2006SINBADapo.pdf},
	author = {Gilles Hennenfent}
}
@conference {hennenfent06SINBADssr,
	title = {A primer on stable signal recovery},
	booktitle = {SINBAD},
	year = {2006},
	publisher = {SINBAD},
	organization = {SINBAD},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/hennenfent06SINBADssr/hennenfent06SINBADssr.pdf},
	author = {Gilles Hennenfent}
}
@conference {herrmann2006SINBADapow,
	title = {A primer on weak conditions for stable recovery},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {During this presentation an introduction will be given on the method of stable recovery from noisy and incomplete data. Weak recovery conditions that guarantee the recovery for typical acquisition geometries will be reviewed and numerical recovery examples will be presented. The advantage of these weak conditions is that they are less pessimistic and {\textquoteleft}verifiable{\textquoteright} or very large-scale acquisition geometries.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/herrmann2006SINBADapow/herrmann2006SINBADapow.pdf},
	author = {Felix J. Herrmann}
}
@conference {thomson2006SINBADppe,
	title = {(P)SLIMPy: parallel extension},
	booktitle = {SINBAD 2006},
	year = {2006},
	abstract = {The parallel extensions to the SLIMpy environment enable pipe-based processing of large data sets in an MPI-based parallel environment. Parallel processing can be done by straightforward slicing of data, or by using an overlapping domain decomposition that requires communication between different processors. The principal aim of the parallel extensions is to leave abstract numerical algorithms (ANA{\textquoteright}s) and applications programmed for use in SLIMpy untouched when moving to parallel processing. The object-oriented functionality of Python makes this possible.},
	keywords = {Presentation, SINBAD, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2006/thomson2006SINBADppe/thomson2006SINBADppe.pdf},
	author = {Darren Thomson}
}
@article {bernabe2004JGRpas,
	title = {Permeability and storativity of binary mixtures of high-and low-porosity materials},
	journal = {Journal of Geophysical Research: Solid Earth},
	volume = {109},
	year = {2004},
	month = {12},
	pages = {B12207},
	abstract = {As a first step toward determining the mixing laws for the transport properties of rocks, we prepared binary mixtures of high- and low-permeability materials by isostatically hot-pressing mixtures of fine powders of calcite and quartz. The resulting rocks were marbles containing varying concentrations of dispersed quartz grains. Pores were present throughout the rock, but the largest ones were preferentially associated with the quartz particles, leading us to characterize the material as being composed of two phases, one with high permeability and the second with low permeability. We measured the permeability and storativity of these materials using the oscillating flow technique, while systematically varying the effective pressure and the period and amplitude of the input fluid oscillation. Control measurements performed using the steady state flow and pulse decay techniques agreed well with the oscillating flow tests. The hydraulic properties of the marbles were highly sensitive to the volume fraction of the high-permeability phase (directly related to the quartz content). Below a critical quartz content, slightly less than 20 wt \%, the high-permeability volume elements were disconnected, and the overall permeability was low. Above the critical quartz content the high-permeability volume elements formed throughgoing paths, and permeability increased sharply. We numerically simulated fluid flow through binary materials and found that permeability approximately obeys a percolation-based mixing law, consistent with the measured permeability of the calcite-quartz aggregates.},
	keywords = {Modeling, permeability, porosity, SLIM},
	doi = {10.1029/2004JB003111},
	url = {https://slim.gatech.edu/Publications/Public/Journals/JournalOfGeophysicalResearch/2004/bernabe04JGRpas/bernabe04JGRpas.pdf},
	url2 = {http://onlinelibrary.wiley.com/doi/10.1029/2004JB003111/abstract},
	author = {Y. Bernab{\'e} and U. Mok and B. Evans and Felix J. Herrmann}
}
