@conference {herrmann2025HPCuam,
	title = {Uncertainty-aware machine-learning enabled velocity-model building with Devito},
	booktitle = {18th Annual Energy High Performance Computing Conference 2025, RICE},
	year = {2025},
	note = {18th Annual High Performance Computing Conference 2025},
	month = {02},
	pages = {4th Annual Devito User and Developer Workshop},
	keywords = {devito, FWI, HPC, inversion, JUDI, machine learning, RICE, software, Uncertainty quantification, velocity model},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/RHPC/2025/herrmann2025HPCuam},
	author = {Felix J. Herrmann}
}
@conference {herrmann2024SIAMUQdt,
	title = {An Uncertainty-Aware Digital Twin for Geological Carbon Storage},
	booktitle = {SIAM Conference on Uncertainty Quantification},
	year = {2024},
	note = {(SIAM UQ, Trieste)},
	month = {03},
	abstract = {Arguably, Geological Carbon Storage constitutes the only truly scalable net-negative carbon emission technology. To mitigate its risks and optimize its operations, an uncertainty-aware Digital Twin is being developed. To leverage existing fluid-flow and seismic simulation and imaging capabilities, the envisioned twin combines techniques from sequential and simulation-based Bayesian inference to train its deep generative neural networks to draw samples from the posterior of the Digital Twin{\textquoteright}s state. Because these samples are conditioned on observed time-lapse field data, these twins are capable of capturing the dynamics of CO2 plumes and their uncertainty.},
	keywords = {Bayesian inference, CCS, deep learning, digital twin, GCS, Imaging, monitoring, SIAM, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMUQ/2024/digital-twin},
	author = {Abhinav Prakash Gahlot and Rafael Orozco and Haoyun Li and Grant Bruer and Ziyi Yin and Mathias Louboutin and Felix J. Herrmann}
}
@conference {yin2023EMIutm,
	title = {Uncertainty-aware time-lapse monitoring of geological carbon storage with learned surrogates},
	booktitle = {Engineering Mechanics Institute Conference},
	year = {2023},
	note = {(EMI, Atlanta)},
	month = {06},
	abstract = {Time-lapse seismic monitoring of CO2 sequestration is computationally expensive as it involves modeling of both fluid-flow physics and wave physics. It also requires differentiation through the solvers with respect to properties of interest in the subsurface. In this talk, we present a learned end-to-end inversion framework, which uses a pre-trained Fourier neural operator as a learned surrogate for the fluid-flow simulator in order to greatly reduces the cost associated with fluid-flow modeling and differentiation through the solver. Through synthetic experiments, we demonstrate the efficacy of this framework on inverting the subsurface permeability of the reservoir and on monitoring CO2 plumes. We further quantify the uncertainty of the permeability and CO2 plumes with conditional normalizing flow. With this framework, we can also forecast the growth of CO2 plumes in the future with uncertainty estimation without any acquired seismic data.},
	keywords = {amortized Bayes, conditional normalizing flows, deep learning, EMI, end-to-end, Fourier neural operators, GCS, inversion, machine learning, Normalizing flows, time-lapse, uncertain quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/yin2023EMIutm.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EMI/2023/yin2023EMIutm/index.html},
	author = {Ziyi Yin and Rafael Orozco and Mathias Louboutin and Ali Siahkoohi and Felix J. Herrmann}
}
@conference {siahkoohi2022INTERSPEECHulbs,
	title = {Ultra-Low-Bitrate Speech Coding with Pretrained Transformers},
	booktitle = {Proceedings of INTERSPEECH},
	year = {2022},
	note = {International Speech Communication Association (ISCA)},
	month = {06},
	abstract = {Speech coding facilitates the transmission of speech over low-bandwidth networks with minimal distortion. Neural-network based speech codecs have recently demonstrated significant improvements in quality over traditional approaches. While this new generation of codecs is capable of synthesizing high-fidelity speech, their use of recurrent or convolutional layers often restricts their effective receptive fields, which prevents them from compressing speech efficiently. We propose to further reduce the bitrate of neural speech codecs through the use of pretrained Transformers, capable of exploiting long-range dependencies in the input signal due to their inductive bias. As such, we use a pretrained Transformer in tandem with a convolutional encoder, which is trained end-to-end with a quantizer and a generative adversarial net decoder. Our numerical experiments show that supplementing the convolutional encoder of a neural speech codec with Transformer speech embeddings yields a speech codec with a bitrate of 600 bps that outperforms the original neural speech codec in synthesized speech quality when trained at the same bitrate. Subjective human evaluations suggest that the quality of the resulting codec is comparable or better than that of conventional codecs operating at three to four times the rate.},
	keywords = {deep learning, GANs, INTERSPEECH, signal processing, Transformers},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/INTERSPEECH/2022/siahkoohi2022INTERSPEECHulbs/paper.pdf},
	author = {Ali Siahkoohi and Michael Chinen and Tom Denton and W. Bastiaan Kleijn and Jan Skoglund}
}
@conference {louboutin2021SEGulm,
	title = {Ultra-low memory seismic inversion with randomized trace estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {787-791},
	abstract = {Inspired by recent work on extended image volumes that lays the ground for randomized probing of extremely large seismic wavefield matrices, we present a memory frugal and computationally efficient inversion methodology that uses techniques from randomized linear algebra. By means of a carefully selected realistic synthetic example, we demonstrate that we are capable of achieving competitive inversion results at a fraction of the memory cost of conventional full-waveform inversion with limited computational overhead. By exchanging memory for negligible computational overhead, we open with the presented technology the door towards the use of low-memory accelerators such as GPUs.},
	keywords = {FWI, HPC, inversion, randomized linear algebra, SEG},
	doi = {10.1190/segam2021-3584072.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/louboutin2021SEGulm/louboutinp.html},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/louboutin2021SEGulm/louboutin2021SEGulm.mp4},
	software = {https://github.com/slimgroup/TimeProbeSeismic.jl},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@conference {siahkoohi2020SEGuqi,
	title = {Uncertainty quantification in imaging and automatic horizon tracking{\textemdash}a Bayesian deep-prior based approach},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {1636-1640},
	abstract = {In inverse problems, uncertainty quantification (UQ) deals with a probabilistic description of the solution nonuniqueness and data noise sensitivity. Setting seismic imaging into a Bayesian framework allows for a principled way of studying uncertainty by solving for the model posterior distribution. Imaging, however, typically constitutes only the first stage of a sequential workflow, and UQ becomes even more relevant when applied to subsequent tasks that are highly sensitive to the inversion outcome. In this paper, we focus on how UQ trickles down to horizon tracking for the determination of stratigraphic models and investigate its sensitivity with respect to the imaging result. As such, the main contribution of this work consists in a data-guided approach to horizon tracking uncertainty analysis. This work is fundamentally based on a special reparameterization of reflectivity, known as "deep prior". Feasible models are restricted to the output of a convolutional neural network with a fixed input, while weights and biases are Gaussian random variables. Given a deep prior model, the network parameters are sampled from the posterior distribution via a Markov chain Monte Carlo method, from which the conditional mean and point-wise standard deviation of the inferred reflectivities are approximated. For each sample of the posterior distribution, a reflectivity is generated, and the horizons are tracked automatically. In this way, uncertainty on model parameters naturally translates to horizon tracking. As part of the validation for the proposed approach, we verified that the estimated confidence intervals for the horizon tracking coincide with geologically complex regions, such as faults.},
	keywords = {horizon picking, Imaging, machine learning, SEG, Uncertainty quantification},
	doi = {10.1190/segam2020-3417560.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGuqi/siahkoohi2020SEGuqi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGuqi/siahkoohi2020SEGuqi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/siahkoohi2020SEGuqi/siahkoohi2020SEGuqi_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {siahkoohi2020SIAMTXLAudg,
	title = {Unsupervised data-guided uncertainty analysis in imaging and horizon tracking},
	booktitle = {SIAM Texas-Louisiana},
	year = {2020},
	note = {SIAMTXLA},
	month = {10},
	abstract = {Imaging typically is the first stage of a sequential workflow, and uncertainty quantification becomes more relevant when applied to subsequent tasks. We propose a Bayesian approach to horizon tracking uncertainty analysis, where we deploy a deep prior instead of adhering to handcrafted priors. By passing samples from the posterior distribution obtained via stochastic gradient Langevin dynamics to an automatic horizon tracker, we are able to incorporate the uncertainty on model parameters into horizon tracking.},
	keywords = {deep learning, horizon tracking, McMC, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMTXLA/2020/siahkoohi2020SIAMTXLAudg/siahkoohi2020SIAMTXLAudg_pres.pdf},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Mathias Louboutin and Felix J. Herrmann}
}
@conference {fang2017WAVESuqf,
	title = {Uncertainty quantification for inverse problems with a weak wave-equation constraint},
	booktitle = {WAVES 2017 {\textendash}- 13th International Conference on Mathematical and Numerical Aspects of Wave Propagation},
	year = {2017},
	note = {(WAVES, Minneapolis)},
	month = {05},
	pages = {127-128},
	abstract = {In this work, we present a new posterior distribution to quantify uncertainties in solutions of wave-equation based inverse problems. By introducing an auxiliary variable for the wavefields, we weaken the strict wave-equation constraint used by conventional Bayesian approaches. With this weak constraint, the new posterior distribution is a bi-Gaussian distribution with respect to both model parameters and wavefields, which can be directly sampled by the Gibbs sampling method.},
	keywords = {constraint, Gibbs sampling, uncertainty, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/WAVES/2017/fang2017WAVESuqf/fang2017WAVESuqf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/WAVES/2017/fang2017WAVESuqf/fang2017WAVESuqf_pres.pdf},
	author = {Zhilong Fang and Curt Da Silva and Rachel Kuske and Felix J. Herrmann}
}
@conference {fang2016SEGuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion using a PDE free semidefinite Hessian and randomize-then-optimize method},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1390-1394},
	abstract = {The data of full-waveform inversion often contains noise, which induces uncertainties in the inversion results. Ideally, one would like to run a number of independent inversions with different realizations of the noise and assess model-side uncertainties from the resulting models, however this is not feasible because we collect the data only once. To circumvent this restriction, various sampling schemes have been devised to generate an ensemble of models that fit the data to within the noise level. Such sampling schemes typically involve running multiple inversions or evaluating the Hessian of the cost function, both of which are computationally expensive. In this work, we propose a new method to quantify uncertainties based on a novel formulation of the full-waveform inversion problem {\textendash} wavefield reconstruction inversion. Based on this formulation, we formulate a semidefinite approximation of the corresponding Hessian matrix. By precomputing certain quantities, we are able to apply this Hessian to given input vectors without additional solutions of the underlying partial differential equations. To generate a sample, we solve an auxiliary stochastic optimization problem involving this Hessian. The result is a computationally feasible method that, with little overhead, can generate as many samples as required at small additional cost. We test our method on the synthetic BG Compass model and compare the results to a direct-sampling approach. The results show the feasibility of applying our method to computing statistical quantities such as the mean and standard deviation in the context of wavefield reconstruction inversion.},
	keywords = {SEG, Uncertainty quantification, WRI},
	doi = {10.1190/segam2016-13879108.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/fang2016SEGuqw/fang2016SEGuqw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/fang2016SEGuqw/fang2016SEGuqw_pres.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {dasilva2016SEGuse,
	title = {A unified 2D/3D software environment for large scale time-harmonic full waveform inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2016},
	note = {(SEG, Dallas)},
	month = {10},
	pages = {1169-1173},
	abstract = {Full-Waveform Inversion is a costly and complex procedure for realistically sized 3D seismic data. The performance-critical nature of this problem often results in software environments that are written entirely in low-level languages, making them hard to understand, maintain, improve, and extend. The unreadability of such codes can stymie research developments, where the translation from higher level mathematical ideas to high performance codes can be lost, inhibiting the uptake of new ideas in production-level codebases. We propose a new software organization paradigm for Full-Waveform Inversion and other PDE-constrained optimization problems that is flexible, efficient, scalable, and demonstrably correct. We decompose the various structural components of FWI in to its constituent components, from parallel computation to assembling objective functions and gradients to lower level matrix-vector multiplications. This decomposition allows us to create a framework where individual components can be easily swapped out to suit a particular user{\textquoteright}s existing software environment. The ease of applying high-level algorithms to the FWI problem allows us to easily implement stochastic FWI and demonstrate its effectiveness on a large scale 3D problem.},
	keywords = {Full-waveform inversion, large scale, numerical analysis, SEG, software},
	doi = {10.1190/segam2016-13869051.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/dasilva2016SEGuse/dasilva2016SEGuse.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2016/dasilva2016SEGuse/dasilva2016SEGuse_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@conference {fang2015EAGEuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {In this work, we propose a method to quantify the uncertainty of wavefield reconstruction inversion under the framework of Bayesian inference. Unlike the conventional method using the wave equation as the forward mapping, we involve the wave equation misfit in the posterior distribution and propose a new posterior distribution. The negative log-likelihood of the new distribution is less oscillatory than that of the conventional posterior distribution, and its Gauss-Newton Hessian is a diagonal matrix that can be generated without any additional computational cost. We use the diagonal Gauss-Newton Hessian to derive an approximate Gaussian distribution at the maximum likelihood point to quantify the uncertainty. This method makes the uncertainty quantification for WRI computationally tractable and is able to provide reasonable uncertainty analysis based on our numerical results.},
	keywords = {EAGE, FWI, UQ, WRI},
	doi = {10.3997/2214-4609.201413198},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/fang2015EAGEuqw/fang2015EAGEuqw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/fang2015EAGEuqw/fang2015EAGEuqw_pres.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@conference {fang2015IIPFWIuqw,
	title = {Uncertainty quantification for Wavefield Reconstruction Inversion},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this work, we propose a method to quantify the uncertainty of wavefield reconstruction inversion under the framework of Bayesian inference. Unlike the conventional method using the wave equation as the forward mapping, we involve the wave equation misfit in the posterior distribution and propose a new posterior distribution. The negative log-likelihood of the new distribution is less oscillatory than that of the conventional posterior distribution, and its Gauss-Newton Hessian is a diagonal matrix that can be generated without any additional computational cost. We use the diagonal Gauss-Newton Hessian to derive an approximate Gaussian distribution at the maximum likelihood point to quantify the uncertainty. This method makes the uncertainty quantification for WRI computationally tractable and is able to provide reasonable uncertainty analysis based on our numerical results.},
	keywords = {FWI, uncertainty, WRI},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/fang2015IIPFWIuqw/fang2015IIPFWIuqw_pres.pdf},
	author = {Zhilong Fang and Chia Ying Lee and Curt Da Silva and Felix J. Herrmann and Rachel Kuske}
}
@conference {oghenekohwo2015EAGEuci,
	title = {Using common information in compressive time-lapse full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The use of time-lapse seismic data to monitor changes in the subsurface has become standard practice in industry. In addition, full-waveform inversion has also been extended to time-lapse seismic to obtain useful time-lapse information. The computational cost of this method are becoming more pronounced as the volume of data increases. Therefore, it is necessary to develop fast inversion algorithms that can also give improved time-lapse results. Rather than following existing joint inversion algorithms, we are motivated by a joint recovery model which exploits the common information among the baseline and monitor data. We propose a joint inversion framework, leveraging ideas from distributed compressive sensing and the modified Gauss-Newton method for full-waveform inversion, by using the shared information in the time-lapse data. Our results on a realistic synthetic example highlight the benefits of our joint inversion approach over a parallel inversion method that does not exploit the shared information. Preliminary results also indicate that our formulation can address time-lapse data with inconsistent acquisition geometries.},
	keywords = {EAGE, FWI, time-lapse},
	doi = {10.3997/2214-4609.201413086},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/oghenekohwo2015EAGEuci/oghenekohwo2015EAGEuci_poster.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@conference {vanderneut2012EAGEdecomp,
	title = {Up / down wavefield decomposition by sparse inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Expressions have been derived for the decomposition of multi-component seismic recordings into up- and down-going constituents. However, these expressions contain singularities at critical angles and can be sensitive for noise. By interpreting wavefield decomposition as an inverse problem and imposing constraints on the sparseness of the solution, we arrive at a robust formalism that can be applied to noisy data. The method is demonstrated on synthetic data with multi-component receivers in a horizontal borehole, but can also be applied for different configurations, including OBC and dual-sensor streamers.},
	keywords = {EAGE, sparse inversion, wavefield decomposition},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanderneut2012EAGEdecomp/vanderneut2012EAGEdecomp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/vanderneut2012EAGEdecomp/vanderneut2012EAGEdecomp_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=58907},
	author = {Joost van der Neut and Felix J. Herrmann}
}
@conference {lin2009SEGucs,
	title = {Unified compressive sensing framework for simultaneous acquisition with primary estimation},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {28},
	year = {2009},
	month = {10},
	pages = {3113-3117},
	abstract = {The central promise of simultaneous acquisition is a vastly improved crew efficiency during acquisition at the cost of additional post-processing to obtain conventional source-separated data volumes. Using recent theories from the field of compressive sensing, we present a way to systematically model the effects of simultaneous acquisition. Our formulation form a new framework in the study of acquisition design and naturally leads to an inversion-based approach for the separation of shot records. Furthermore, we show how other inversion-based methods, such as a recently proposed method from van Groenestijn and Verschuur (2009) for primary estimation, can be processed together with the demultiplexing problem to achieve a better result compared to a separate treatment of these problems.},
	keywords = {Presentation, SEG},
	doi = {10.1190/1.3255502},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/lin09SEGucs/lin09SEGucs.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2009/lin09SEGucs/lin09SEGucs_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
