@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 {oghenekohwo2017EAGEitl,
	title = {Improved time-lapse data repeatability with randomized sampling and distributed compressive sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Recently, new ideas on randomized sampling for time-lapse seismic acquisition have been proposed to address some of the challenges of replicating time-lapse surveys. These ideas, which stem from distributed compressed sensing (DCS) led to the birth of a joint recovery model (JRM) for processing time-lapse data (noise-free) acquired from non-replicated acquisition geometries. However, when the earth does not change{\textendash}-i.e. no time-lapse{\textemdash}the recovered vintages from two non-replicated surveys should show high repeatability measured in terms of normalized RMS, which is a standard metric for quantifying time-lapse data repeatability. Under this assumption of no time-lapse change, we demonstrate improved repeatability (with JRM) of the recovered data from non-replicated random samplings, first with noisy data and secondly in situations where there are calibration errors i.e. where the acquisition parameters such as source/receiver coordinates are not precise.},
	keywords = {calibration, Compressive Sensing, EAGE, noise, repeatability, time lapse},
	doi = {10.3997/2214-4609.201701389},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/oghenekohwo2017EAGEitl/oghenekohwo2017EAGEitl.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/oghenekohwo2017EAGEitl/oghenekohwo2017EAGEitl_poster.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {wason2015EAGEcsm,
	title = {Compressed sensing in 4-D marine{\textendash}-recovery of dense time-lapse data from subsampled data without repetition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {We present an extension of our time-jittered marine acquisition for time-lapse surveys by working on more realistic field acquisition scenarios by incorporating irregular spatial grids without insisting on repeatability between the surveys. Since we are always subsampled in both the baseline and monitor surveys, we are interested in recovering the densely sampled baseline and monitor, and then the (complete) 4-D difference from subsampled/incomplete baseline and monitor data.},
	keywords = {EAGE, NFFT, off-the-grid, simultaneous acquisition, time-lapse},
	doi = {10.3997/2214-4609.201413088},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/wason2015EAGEcsm/wason2015EAGEcsm_poster.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@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 {petrenko2014EAGEaih,
	title = {Accelerating an iterative Helmholtz solver with FPGAs},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {We implement the Kaczmarz row-projection algorithm (Kaczmarz (1937)) on a CPU host + FPGA accelerator platform using techniques of dataflow programming. This algorithm is then used as the preconditioning step in CGMN, a modified version of the conjugate gradients method (Bj{\"o}rck and Elfving (1979)) that we use to solve the time-harmonic acoustic isotropic constant density wave equation. Using one accelerator we achieve a speed-up of over 2{\texttimes} compared with one Intel core.},
	keywords = {CGMN, EAGE, FPGA, Helmholtz equation, Kaczmarz, reconfigurable computing},
	doi = {10.3997/2214-4609.20141141},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/petrenko2014EAGEaih/petrenko2014EAGEaih_pres.pdf},
	author = {Art Petrenko and Tristan van Leeuwen and Diego Oriato and Simon Tilbury and Felix J. Herrmann}
}
@conference {herrmann2014EAGEWSrrt,
	title = {Randomization and repeatability in time-lapse marine acquisition},
	booktitle = {EAGE Workshop on Land and Ocean Bottom; Broadband Full Azimuth Seismic Surveys; Spain},
	year = {2014},
	note = {(EAGE Workshop, Spain)},
	month = {04},
	keywords = {4D seismic, EAGE, Marine acquisition, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/herrmann2014EAGEWSrrt.pdf},
	author = {Haneet Wason and Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2014EAGEtls,
	title = {Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In the current paradigm of 4-D seismic, guaranteeing repeatability in acquisition and processing of the baseline and monitor surveys ranks highest amongst the technical challenges one faces in detecting time-lapse signals. By using recent insights from the field of compressive sensing, we show that the condition of survey repeatability can be relaxed as long as we carry out a sparsitypromoting program that exploits shared information between the baseline and monitor surveys. By inverting for the baseline and monitor survey as the common "background", we are able to compute high-fidelity 4-D differences from carefully selected synthetic surveys that have different sets of source/receivers missing. This synthetic example is proof of concept of an exciting new approach to randomized 4-D acquisition where time-lapse signal can be computed as long as the survey details, such as source/receiver locations are known afterwards.},
	keywords = {4-D seismic, EAGE, joint recovery, time-lapse},
	doi = {10.3997/2214-4609.20141478},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls_pres.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@conference {min2012EAGEefwi,
	title = {Frequency-domain elastic waveform inversion using weighting factors related to source-deconvolved residuals},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {One of the limitations in seismic waveform inversion is that inversion results are very sensitive to initial guesses, which may be because the gradients computed at each frequency are not properly weighted depending on given models.Analyzingthe conventional waveform inversion algorithms using the pseudo-Hessian matrix as a pre-conditioner shows that the gradientsdo not properly describe the feature of given models or high- and low-end frequencies do not contribute the model parameter updates due to banded spectra of source wavelet. For a better waveform inversion algorithm, we propose applying weighting factors to gradients computed at each frequency. The weighting factors are designed using the source-deconvolved back-propagated wavefields. Numerical results for the SEG/EAGE salt model show that the weighting method improves gradient images and its inversion results are compatible with true velocities even with poorly estimated initial guesses.},
	keywords = {EAGE, elastic, frequency-domain, waveform inversion, weighting factors},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/min2012EAGEefwi/min2012EAGEefwi.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59623},
	author = {Ju-Won Oh and Dong-Joo Min and Felix J. Herrmann}
}
