@conference {zeng2025IMAGEw2f,
	title = {Well2Flow: Reconstruction of reservoir states from sparse wells using score-based generative models},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {deep learning, diffusion-pde models, Generative models, history matching, IMAGE, reservoir simulation, score, SEG, sparse observations, Uncertainty quantification},
	doi = {10.48550/arXiv.2504.06305},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/zeng2025IMAGEw2f},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/zeng2025IMAGEw2f/IMAGE_Shiqin.pdf},
	author = {Shiqin Zeng and Haoyun Li and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@article {yin2024wiser,
	title = {WISER: multimodal variational inference for full-waveform inversion without dimensionality reduction},
	journal = {Geophysics},
	volume = {90},
	year = {2025},
	note = {(GEOPHYSICS)},
	month = {03},
	abstract = {We present a semi-amortized variational inference framework designed for computationally feasible uncertainty quantification in 2D full-waveform inversion to explore the multimodal posterior distribution without dimensionality reduction. The framework is called WISER, short for full-Waveform variational Inference via Subsurface Extensions with Refinements. WISER leverages the power of generative artificial intelligence to perform approximate amortized inference that is low-cost albeit showing an amortization gap. This gap is closed through non-amortized refinements that make frugal use of acoustic wave physics. Case studies illustrate that WISER is capable of full-resolution, computationally feasible, and reliable uncertainty estimates of velocity models and imaged reflectivities.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Imaging, Inverse problems, MVA, Summary Statistics, Uncertainty quantification, WISE, WISER},
	doi = {10.1190/geo2024-0483.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2024/yin2024wiser/WISER.html},
	author = {Ziyi Yin and Rafael Orozco and Felix J. Herrmann}
}
@article {yin2023wise,
	title = {WISE: full-Waveform variational Inference via Subsurface Extensions},
	journal = {Geophysics},
	volume = {89},
	year = {2024},
	note = {(GEOPHYSICS)},
	month = {04},
	abstract = {We introduce a probabilistic technique for full-waveform inversion, employing variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, Geophysics, Imaging, Inverse problems, MVA, RTM, Summary Statistics, Uncertainty quantification, WISE},
	doi = {10.1190/geo2023-0744.1},
	url = {https://slim.gatech.edu/Publications/Public/Journals/Geophysics/2024/yin2023wise/paper.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yin2024IMAGEwiser,
	title = {WISER: full-Waveform variational Inference via Subsurface Extensions with Refinements},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2024},
	month = {8},
	abstract = {We introduce a cost-effective Bayesian inference method for full-waveform inversion (FWI) to quantify uncertainty in migration-velocity models and its impact on imaging. Our method targets inverse uncertainty due to null-space of the wave modeling operators and observational noise, and forward uncertainty where the uncertainty in velocity models is propagated to uncertainty in amplitude and positioning of imaged reflectivities. This is achieved by integrating generative artificial intelligence (genAI) with physics-informed common-image gathers (CIGs), which greatly reduces reliance on accurate initial FWI-velocity models. In addition, we illustrate the capability of fine-tuning the generative AI networks with frugal physics-based refinements to improve the inference accuracy.},
	keywords = {Amortized Variational Inference, Bayesian inference, CIG, conditional normalizing flows, deep learning, FWI, IMAGE, Imaging, Inverse problems, MVA, RTM, SEG, Summary Statistics, Uncertainty quantification, WISE, WISER},
	url = {https://slimgroup.github.io/IMAGE2024/yin2024SEG/paper.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@article {louboutin2023rte,
	title = {Wave-based inversion at scale on GPUs with randomized trace estimation},
	journal = {Geophysical Prospecting},
	year = {2023},
	note = {(Geophysical Prospecting)},
	month = {07},
	abstract = {Thanks to continued performance improvements in software and hardware, wave-equation based imaging technologies, such full-waveform inversion and reverse-time migration, are becoming more common place. However, wide-spread adaptation of these advanced imaging modalities has not yet materialized because current implementations are not able to reap the full benefits from accelerators, in particular those offered by memory-scarce graphics processing units. Through the use of randomized trace estimation, we overcome the memory bottleneck of this type of hardware. At the cost of limited computational overhead and controllable incoherent errors in the gradient, the memory footprint of adjoint-state methods is reduced drastically. Thanks to this relatively simple to implement memory reduction via an approximate imaging condition, we are able to benefit from graphics processing units without memory offloading. We demonstrate the performance of the proposed algorithm on acoustic 2- and 3-D full-waveform inversion examples and on the formation of image gathers in transverse tilted isotropic media.},
	keywords = {3D, CIG, FWI, GPUs, Random trace, stochastic},
	doi = {10.1111/1365-2478.13405},
	url = {https://slim.gatech.edu/Publications/Public/Journals/GeophysicalProspecting/2023/louboutin2023rte/paper.html},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@presentation {yin2023ML4SEISMICwise,
	title = {WISE: Full-waveform Inference with Subsurface Extensions},
	year = {2023},
	month = {11},
	abstract = {Quantifying uncertainty in full-waveform inversion is complex given the large sizes of both the model and data. A previous approach employed a variational inference framework, leveraging reverse-time migration to summarize observed data and approximate the posterior distribution through conditional normalizing flows. While reverse-time migration effectively summarizes the data when the background model is close to the true one, its accuracy diminishes with a less accurate background model. In our study, we suggest utilizing subsurface offset gathers as the summary statistics for the variational inference of full-waveform inversion. These gathers retain all the information in seismic data, even when the background model is cycle-skipped or fails to flatten the gathers. Through a case study on Compass model, we confirm our framework{\textquoteright}s effectiveness and show that subsurface offset gathers offer a better summary statistic than just reverse-time migration.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, FWI, inversion, machine learning, ML4SEISMIC, Normalizing flows, SLIM, Summary Statistics, uncertain quantification, Variational Inference},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/ML4SEISMIC/2023/yin2023ML4SEISMICwise},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Felix J. Herrmann}
}
@conference {siahkoohi2022EAGEweb,
	title = {Wave-equation based inversion with amortized variational Bayesian inference},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {Session 2: Velocity model building and imaging (different domains)},
	abstract = {Solving inverse problems involving measurement noise and modeling errors requires regularization in order to avoid data overfit. Geophysical inverse problems, in which the Earth{\textquoteright}s highly heterogeneous structure is unknown, present a challenge in encoding prior knowledge through analytical expressions. Our main contribution is a generative-model-based regularization approach, robust to out-of-distribution data, which exploits the prior knowledge embedded in existing data and model pairs. Utilizing an amortized variational inference objective, a conditional normalizing flow (NF) is pretrained on pairs of low- and high-fidelity migrated images in order to achieve a low-fidelity approximation to the seismic imaging posterior distribution for previously unseen data. The NF is used after pretraining to reparameterize the unknown seismic image in an inversion scheme involving physics-guided data misfit and a Gaussian prior on the NF latent variable. Solving this optimization problem with respect to the latent variable enables us to leverage the benefits of data-driven conditional priors whilst being informed by physics and data. The numerical experiments demonstrate that the proposed inversion scheme produces seismic images with limited artifacts when dealing with noisy and out-of-distribution data.},
	keywords = {conditional priors, EAGE, Normalizing flows, seismic imaging},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/siahkoohi2022EAGEweb/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/siahkoohi2022EAGEweb/Mon-Herrmann.pdf},
	software = {https://github.com/slimgroup/ConditionalNFs4Imaging.jl},
	author = {Ali Siahkoohi and Rafael Orozco and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {zhang2020SEGwrw,
	title = {Wavefield recovery with limited-subspace weighted matrix factorizations},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {2858-2862},
	abstract = {Modern-day seismic imaging and monitoring technology increasingly rely on dense full-azimuth sampling. Unfortunately, the costs of acquiring densely sampled data rapidly become prohibitive and we need to look for ways to sparsely collect data, e.g. from sparsely distributed ocean bottom nodes, from which we then derive densely sampled surveys through the method of wavefield reconstruction. Because of their relatively cheap and simple calculations, wavefield reconstruction via matrix factorizations has proven to be a viable and scalable alternative to the more generally used transform-based methods. While this method is capable of processing all full azimuth data frequency by frequency slice, its performance degrades at higher frequencies because monochromatic data at these frequencies is not as well approximated by low-rank factorizations. We address this problem by proposing a recursive recovery technique, which involves weighted matrix factorizations where recovered wavefields at the lower frequencies serve as prior information for the recovery of the higher frequencies. To limit the adverse effects of potential overfitting, we propose a limited-subspace recursively weighted matrix factorization approach where the size of the row and column subspaces to construct the weight matrices is constrained. We apply our method to data collected from the Gulf of Suez, and our results show that our limited-subspace weighted recovery method significantly improves the recovery quality.},
	keywords = {algorithm, data reconstruction, frequency-domain, interpolation, Processing, SEG},
	doi = {10.1190/segam2020-3428365.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/zhang2020SEGwrw/zhang2020SEGwrw_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Yijun Zhang and Shashin Sharan and Oscar Lopez and Felix J. Herrmann}
}
@conference {siahkoohi2020SEGwdp,
	title = {Weak deep priors for seismic imaging},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {2998-3002},
	abstract = {Incorporating prior knowledge on model unknowns of interest is essential when dealing with ill-posed inverse problems due to the nonuniqueness of the solution and data noise. Unfortunately, it is not trivial to fully describe our priors in a convenient and analytical way. Parameterizing the unknowns with a convolutional neural network (CNN), and assuming an uninformative Gaussian prior on its weights, leads to a variational prior on the output space that favors "natural" images and excludes noisy artifacts, as long as overfitting is prevented. This is the so-called deep-prior approach. In seismic imaging, however, evaluating the forward operator is computationally expensive, and training a randomly initialized CNN becomes infeasible. We propose, instead, a weak version of deep priors, which consists of relaxing the requirement that reflectivity models must lie in the network range, and letting the unknowns deviate from the network output according to a Gaussian distribution. Finally, we jointly solve for the reflectivity model and CNN weights. The chief advantage of this approach is that the updates for the CNN weights do not involve the modeling operator, and become relatively cheap. Our synthetic numerical experiments demonstrate that the weak deep prior is more robust with respect to noise than conventional least-squares imaging approaches, with roughly twice the computational cost of reverse-time migration, which is the affordable computational budget in large-scale imaging problems.},
	keywords = {deep prior, machine learning, SEG, seismic imaging},
	doi = {10.1190/segam2020-3417568.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGwdp/siahkoohi2020SEGwdp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGwdp/siahkoohi2020SEGwdp_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGwdp/siahkoohi2020SEGwdp_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@presentation {herrmann2016SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2016},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2016, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2016/Fall/herrmann2016SINBADFwom/herrmann2016SINBADFwom.mov},
	author = {Felix J. Herrmann}
}
@conference {herrmann2015SIAMwri,
	title = {Wavefield Reconstruction Inversion {\textendash} reaping the benefits from extending the search space},
	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},
	keywords = {SIAM, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2015/herrmann2015SIAMwri/herrmann2015SIAMwri_pres.pdf},
	author = {Felix J. Herrmann}
}
@conference {herrmann2015IIPFWIwcc,
	title = {Wavefield Reconstruction Inversion with convex constraints},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {During this talk, we discuss how to exploit the special structure of Wavefield Reconstruction Inversion (WRI) to include convex bound and total-variation constraints in a computationally feasible manner. The resulting method shows promising results on challenging models that include high-velocity high-contrast inclusions such as salt or basalt. This is joint work with Ernie Esser who passed away and who is dearly missed.},
	keywords = {convex, FWI, regularization},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/herrmann2015IIPFWIwcc/herrmann2015IIPFWIwcc_pres.pdf},
	author = {Felix J. Herrmann and Ernie Esser and Llu{\'\i}s Guasch}
}
@presentation {fang2015SINBADwri,
	title = {Wavefield reconstruction inversion with source estimation and its application to 2014 Chevron synthetic blind test dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data augmentation problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADSPRING2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Spring/fang2015SINBADwri/fang2015SINBADwri.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@conference {fang2015IIPFWIwri,
	title = {Wavefield Reconstruction Inversion with source estimation and its application to 2014 Chevron synthetic blind test dataset},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data augmentation problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {FWI, source estimation, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/fang2015IIPFWIwri/fang2015IIPFWIwri_pres.pdf},
	author = {Zhilong Fang and Felix J. Herrmann}
}
@conference {wang2015SIAMwds,
	title = {Wavefield-denoising and source encoding},
	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},
	keywords = {SIAM, source encoding, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SIAM/2015/wang2015SIAMwds/wang2015SIAMwds_pres.pdf},
	author = {Rongrong Wang and Ozgur Yilmaz and Felix J. Herrmann}
}
@conference {peters2015AIPwri,
	title = {Wavefield-reconstruction inversion},
	booktitle = {Conference on Applied Inverse Problems},
	year = {2015},
	note = {(AIP, Helsinki)},
	month = {05},
	abstract = {Wavefield Reconstruction Inversion is a method for PDE-constrained optimization, which revolves around the estimation of fields using the PDE as well as the observed data in a least-squares sense. The method is quadratic penalty based, which offers some interesting possibilities for the construction of algorithms, compared to the Lagrangian form. One of the main benefits of the method is when the initial guess is far from the global minimizer. Reduced-space and full-space algorithms are discussed, including illustrative examples. The method was developed with seismic applications in mind, but applies to other PDE-constrained optimization problems as well.},
	keywords = {AIP, Full-waveform inversion, Optimization, penalty method, Waveform reconstruction inversion},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2015/peters2015AIPwri/peters2015AIPwri.pdf},
	author = {Bas Peters and Felix J. Herrmann}
}
@presentation {fang2015SINBADFwri,
	title = {Wavefield-reconstruction inversion with source estimation and minimum smoothness constraint {\textendash} application to the Chevron 2014 dataset},
	year = {2015},
	publisher = {SINBAD},
	abstract = {We present a robust wavefield reconstruction inversion with source estimation. The source wavelet is estimated with the reconstruction of wavefield simultaneously by solving an extended data-augmented problem. We apply this method to the 2014 Chevron synthetic blind test dataset and show the robustness of our method.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/fang2015SINBADFwri/fang2015SINBADFwri.mov},
	author = {Zhilong Fang and Bas Peters and Felix J. Herrmann}
}
@conference {herrmann2015IIPFWIwri,
	title = {Wavefield-Reconstruction Inversion {\textemdash} WRI},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textemdash} which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash} WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {FWI, inversion, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/herrmann2015IIPFWIwri/herrmann2015IIPFWIwri_pres.pdf},
	author = {Felix J. Herrmann}
}
@mastersthesis {li2015THwmd,
	title = {A weighted $\ell_1$-minimization for distributed compressive sensing},
	year = {2015},
	note = {(MSc)},
	month = {09},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Distributed Compressive Sensing (DCS) studies the recovery of jointly sparse signals. Compared to separate recovery, the joint recovery algorithms in DCS are usually more effective as they make use of the joint sparsity. In this thesis, we study a weighted l1-minimization algorithm for the joint sparsity model JSM-1 proposed by Baron et al. Our analysis gives a sufficient null space property for the joint sparse recovery. Furthermore, this property can be extended to stable and robust settings. We also presents some numerical experiments for this algorithm.},
	keywords = {distributed compressive sensing, MSc, thesis, weighted $\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/li2015THwmd/li2015THwmd.pdf},
	author = {Xiaowei Li}
}
@presentation {herrmann2015SINBADFwom,
	title = {Welcome \& Overview of the Meeting},
	year = {2015},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/herrmann2015SINBADFwom/herrmann2015SINBADFwom.mov},
	author = {Felix J. Herrmann}
}
@conference {peters2014EAGEweb,
	title = {Wave-equation based inversion with the penalty method: adjoint-state versus wavefield-reconstruction inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this paper we make a comparison between wave-equation based inversions based on the adjoint-state and penalty methods. While the adjoint-state method involves the minimization of a data-misfit and exact solutions of the wave-equation for the current velocity model, the penalty-method aims to first find a wavefield that jointly fits the data and honours the physics, in a least-squares sense. Given this reconstructed wavefield, which is a proxy for the true wavefield in the true model, we calculate updates for the velocity model. Aside from being less nonlinear{\textendash}the acoustic wave equation is linear in the wavefield and model parameters but not in both{\textendash}the inversion is carried out over a solution space that includes both the model and the wavefield. This larger search space allows the algortihm to circumnavigate local minima, very much in the same way as recently proposed model extentions try to acomplish. We include examples for low frequencies, where we compare full-waveform inversion results for both methods, for good and bad starting models, and for high frequencies where we compare reverse-time migration with linearized imaging based on wavefield-reconstruction inversion. The examples confirm the expected benefits of the proposed method.},
	keywords = {EAGE, Full-waveform inversion, Imaging, Optimization, penalty method},
	doi = {10.3997/2214-4609.20140704},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/peters2014EAGEweb/peters2014EAGEweb.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/peters2014EAGEweb/peters2014EAGEweb_pres.pdf},
	author = {Bas Peters and Felix J. Herrmann and Tristan van Leeuwen}
}
@conference {herrmann2014SEGWS,
	title = {Wavefield Reconstruction Inversion (WRI) {\textendash} a new take on wave-equation based inversion},
	booktitle = {SEG Workshop on Full Waveform Inversion - Elastic Approaches and Issues with Anisotropy, Nonshallow Inversion, Poor Starting Model; Denver},
	year = {2014},
	note = {(SEG Workshop, Denver)},
	month = {10},
	keywords = {FWI, SEG, workshop, WRI},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2014/herrmann2014SEGWSwri/herrmann2014SEGWSwri_pres.pdf},
	author = {Felix J. Herrmann and Ernie Esser and Tristan van Leeuwen and Bas Peters}
}
@presentation {herrmann2014SINBADwri,
	title = {Wavefield Reconstruction Inversion (WRI) - a new take on wave-equation based inversion},
	year = {2014},
	publisher = {SINBAD},
	abstract = {We discuss a recently proposed novel method for waveform inversion: Wavefield Reconstruction Inversion (WRI). As opposed to conventional FWI {\textendash}- which attempts to minimize the error between observed and predicted data obtained by solving a wave equation {\textendash}- WRI reconstructs a wave-field from the data and extracts a model-update from this wavefield by minimizing the wave-equation residual. The method does not require explicit computation of an adjoint wavefield as all the necessary information is contained in the reconstructed wavefield. We show how the corresponding model updates can be interpreted physically analogously to the conventional imaging-condition-based approach.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/herrmann2014SINBADwri/herrmann2014SINBADwri.pdf},
	author = {Felix J. Herrmann}
}
@presentation {ghadermarzy2014SINBADwrr,
	title = {Wavefield reconstruction via randomized sampling},
	year = {2014},
	publisher = {SINBAD},
	abstract = {Regular sampling along the time axis is a common approach for seismic imaging. However, seismic data are often spatially undersampled due to economical reasons as well as ground surface limitations. Operational conditions might result in noisy traces or even gaps in coverage and irregular sampling which often leads to image artifacts. Seismic trace interpolation aims to interpolate missing traces in an otherwise regularly sampled seismic data to a complete regular grid. In this talk, we explain some techniques, with low computational complexity, that can be used to improve the results. In particular, we talk about weighting, switching to midpoint-offset domain from frequency-space domain, and uniform versus jittered undersampling. We illustrate the advantages of these modifications using a real seismic line from the Gulf of Suez.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/ghadermarzy2014SINBADwrr/ghadermarzy2014SINBADwrr.pdf},
	author = {Navid Ghadermarzy and Ozgur Yilmaz and Felix J. Herrmann}
}
@presentation {li2014SINBADwmg,
	title = {Why the modified Gauss-Newton method?},
	year = {2014},
	publisher = {SINBAD},
	abstract = {In our earlier work, we develop the modified Gauss-Newton method for FWI, which requires each update of FWI to be sparse in the curvelet domain. Our empirical observation of the MGN method is that it can find us a solution for FWI problem with sparse perturbation of the initial guess without changing the underlying objective. In this talk, we will analyze the MGN method to find out the reason why it can generate a sparse perturbation of the initial model, because sum of spares updates could easily generate non-sparse perturbations. Moreover, we will illustrate when do we expect the modified Gauss-Newton method to yield a solution with sparse perturbation and in what circumstances should we use it in place of other algorithms like standard Gauss-Newton.},
	keywords = {Presentation, SINBAD, SINBADFALL2014, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2014/Fall/li2014SINBADwmg/li2014SINBADwmg.pdf},
	author = {Xiang Li and Ernie Esser and Felix J. Herrmann}
}
@techreport {li2013EAGEwebmplijsp,
	title = {Wave-equation based multi-parameter linearized inversion with joint-sparsity promotion},
	number = {TR-EOAS-2013-1},
	year = {2013},
	month = {01},
	institution = {UBC},
	abstract = {The successful application of linearized inversion is affected by the prohibitive size of the data, computational resources required, and how accurately the model parameters reflects the real Earth properties. The issue of data size and computational resources can be addressed by combining ideas from sparsity promoting and stochastic optimization, which can allow us to invert model perturbation with a small subset of the data, yielding a few PDE solves for the inversion. In this abstract, we are aiming at addressing the issue of accuracy of model parameters by inverting density and velocity simultaneously rather than only using velocity. As a matter of face, the effects of density and velocity variations towards the wavefield are very similar, which will cause energy leakage between density and velocity images. To overcome this issue, we proposed a incoherence enhanced method that can reduce the similarity between the effect of density and velocity. Moreover, the location of structural variations in velocity and density are often overlapped in geological setting, thus in this abstract, we also exploit this property with joint-sparsity promoting to further improve the imaging result.},
	keywords = {incoherence enhancement, joint-sparsity, Linearized inversion},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/li2013EAGEwebmplijsp/li2013EAGEwebmplijsp.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@presentation {kumar2013SINBADwrs,
	title = {Wavefield reconstruction with SVD-free low-rank matrix factorization},
	year = {2013},
	publisher = {SINBAD},
	abstract = {As shown in past, we can leverage the ideas from the field of compressed sensing to cast problems like seismic data interpolation or sequential shot data recovery from simultaneous data, as a compressed sensing problem. In this work we will show how we can borrow the same ideas of compressed sensing and cast these problems as matrix completion problems. Instead of sparsity we will show that we can exploit the low-rank structure of seismic data to solve these problems. One of the impediments in rank-minimization problem is the computation of singular values. We will also show how we can solve the rank minimization problems SVD-free. The practical application is divided into three parts: 1. In case of sequential seismic data acquisition, how jittered subsampling helps to recover the better quality data as compared to random subsampling. 2. How the incorporation of reciprocity principles help to enhance the quality of recovered fully sampled data. 3. How we can recover the sequential source data from simultaneous source data.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/kumar2013SINBADwrs/kumar2013SINBADwrs.pdf},
	author = {Rajiv Kumar and Aleksandr Y. Aravkin and Hassan Mansour and Ernie Esser and Felix J. Herrmann}
}
@presentation {xiang2012SINBADweb,
	title = {Wave-equation based inversion with joint sparsity promotion},
	year = {2012},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/xiang2012SINBADweb/xiang2012SINBADweb_pres.pdf},
	author = {Xiang Li}
}
@conference {vanleeuwen2012EAGEext,
	title = {Wave-equation extended images: computation and velocity continuation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {An extended image is a multi-dimensional correlation of source and receiver wavefields. For a kinematically correct velocity, most of the energy will be concentrated at zero offset. Because of the computational cost involved in correlating the wavefields for all offsets, such exteded images are computed for a subsurface offset that is aligned with the local dip. In this paper, we present an efficient way to compute extended images for all subsurface offsets without explicitly calculating the receiver wavefields, thus making it computationally feasible to compute such extended images. We show how more conventional image gathers, where the offset is aligned with the dip, can be extracted from this extended image. We also present a velocity continuation procedure that allows us to compute the extended image for a given velocity without recomputing all the source wavefields.},
	keywords = {EAGE, extended image, velocity continuation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanleeuwen2012EAGEext/vanleeuwen2012EAGEext.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanleeuwen2012EAGEext/vanleeuwen2012EAGEext_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59616},
	author = {Tristan van Leeuwen and Felix J. Herrmann}
}
@presentation {yilmaz2012SINBADwms,
	title = {Weighted methods in sparse recovery},
	year = {2012},
	publisher = {SINBAD},
	abstract = {In the recent years we have successfully employed "weighted" algorithms to recover sparse signals from few linear, non-adaptive measurements. The general principle here is to use prior knowledge about the signal to be recovered, e.g., approximate locations of large-in-magnitude transform coefficients, if such information is available. An example for this is the use of weighted 1-norm minimization to improve wavefield reconstruction from randomized (sub)sampling. We will review these results and outline some new directions we have explored during the last year, such as weighted non-convex sparse recovery (see Ghadermarzy{\textquoteright}s talk), weighted analysis-based recovery (see Hargreaves{\textquoteright}s talk), and a weighted randomized Kaczmarz algorithm for solving large overdetermined systems of equations that are known to admit a (nearly) sparse solution. Various examples in seismic will be shown.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/yilmaz2012SINBADwms/yilmaz2012SINBADwms_pres.pdf},
	author = {Ozgur Yilmaz}
}
@presentation {krislock2012SINBADwsn,
	title = {Wireless sensor network localization},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Locating the position of sensors connected together in a wireless network given only the position of a small number of the sensors and estimates of some of the distances between the sensors is a difficult problem with many modern applications. Within the last few years research in wireless sensor network localization has greatly increased due to the many new applications using wireless sensors, from lightweight sensors used to monitor the environment to ocean-bottom sensors used in geophysical applications. A second reason for this increased interest is our recent ability to efficiently solve these problems using modern semidefinite optimization solvers. We will discuss how semidefinite optimization can be used to solve such problems and possible directions for future work.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/krislock2012SINBADwsn/krislock2012SINBADwsn_pres.pdf},
	author = {Nathan Krislock}
}
@presentation {vanleeuwen2011SINBADoofwi,
	title = {Wavefield modelling and inversion in Matlab},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/vanleeuwen2011SINBADoofwi/vanleeuwen2011SINBADoofwi_pres.pdf},
	author = {Tristan van Leeuwen}
}
@conference {Mansour11TRwmmw,
	title = {Weighted -$\ell_1$ minimization with multiple weighting sets},
	booktitle = {Proc. SPIE},
	volume = {8138},
	year = {2011},
	month = {09},
	pages = {813809-813809-13},
	abstract = {In this paper, we study the support recovery conditions of weighted -$\ell_1$ minimization for signal reconstruction from compressed sensing measurements when multiple support estimate sets with different accuracy are available. We identify a class of signals for which the recovered vector from -$\ell_1$ minimization provides an accurate support estimate. We then derive stability and robustness guarantees for the weighted -$\ell_1$ minimization problem with more than one support estimate. We show that applying a smaller weight to support estimate that enjoy higher accuracy improves the recovery conditions compared with the case of a single support estimate and the case with standard, i.e., non-weighted,-$\ell_1$ minimization. Our theoretical results are supported by numerical simulations on synthetic signals and real audio signals.},
	keywords = {Compressive Sensing, Optimization},
	doi = {10.1117/12.894165},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SPIE/2011/Mansour11TRwmmw/Mansour11TRwmmw.pdf},
	author = {Hassan Mansour and Ozgur Yilmaz}
}
@presentation {herrmann2011SINBADoverview,
	title = {Welcome and overview of SINBAD \& DNOISE},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/herrmann2011SINBADoverview/herrmann2011SINBADoverview_pres.pdf},
	author = {Felix J. Herrmann}
}
@presentation {mansour2011SINBADwcw,
	title = {Why do curvelets work?},
	year = {2011},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2011, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2011/Fall/mansour2011SINBADwcw/mansour2011SINBADwcw_pres.pdf},
	author = {Hassan Mansour}
}
@presentation {vanleeuwen2010SINBADwis,
	title = {Waveform inversion by stochastic optimization},
	year = {2010},
	publisher = {SINBAD},
	keywords = {Presentation, SINBAD, SINBADFALL2010, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2010/Fall/vanleeuwen2010SINBADwis/vanleeuwen2010SINBADwis_pres.pdf},
	author = {Tristan van Leeuwen}
}
@conference {yan2008SINBADwru,
	title = {Wavefield reconstruction using simultaneous denoising interpolation vs. denoising after interpolation},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {This report represents and compares two methods of wavefield reconstruction from noisy seismic data with missing traces. The two methods are (i) First interpolate incomplete noisy data to get complete noisy data and then denoise, and (ii) Interpolate and denoise the incomplete noisy data simultaneously. A sample test of synthetic data will be presented. The results of tests show that denoising after interpolation is better than simultaneous denoising and interpolation if the parameter of the denoising problem is chosen appropriately.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/yan2008SINBADwru/yan2008SINBADwru.pdf},
	author = {Jiupeng Yan}
}
