@conference {erdinc2023IMAGEecl,
	title = {Enhancing CO2 Leakage Detectability via Dataset Augmentation},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2023},
	note = {(IMAGE, Houston)},
	month = {08},
	abstract = {Previous work showed that neural classifiers can be trained to detect CO2 leakage from time-lapse seismic images. While this result is crucial to the global deployment of geological carbon storage (GCS), its success depends on relatively dense non-replicated time-lapse data acquisition. In this study, we show that by augmenting the training set with various coarse receiver samplings and corresponding seismic images, we can improve the leakage detection capabilities and accuracy while increasing the robustness with respect to low-cost coarse receiver samplings, e.g. ocean bottom nodes (OBNs).},
	keywords = {CCS, classifier, deep learning, leakage detection, SEG},
	url = {https://slimgroup.github.io/IMAGE2023/DetectabilityWithVision/abstract.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2023/erdinc2023IMAGEecl},
	author = {Huseyin Tuna Erdinc and Abhinav Prakash Gahlot and Mathias Louboutin and Felix J. Herrmann}
}
@conference {louboutin2022EAGEewi,
	title = {Enabling wave-based inversion on GPUs with randomized trace estimation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2022},
	note = {(EAGE, Madrid)},
	month = {06},
	pages = {Seismic Wave Modelling and Least Square Migration 2 session},
	abstract = {By building on recent advances in the use of randomized trace estimation to drastically reduce the memory footprint of adjoint-state methods, we present and validate an imaging approach that can be executed exclusively on accelerators. Results obtained on field-realistic synthetic datasets, which include salt and anisotropy, show that our method produces high-fidelity images. These findings open the enticing perspective of 3D wave-based inversion technology with a memory footprint that matches the hardware and that runs exclusively on clusters of GPUs without the undesirable need to offload certain tasks to CPUs.},
	keywords = {EAGE, Image Volumes, inversion, RTM, SEAM, stochastic, TTI},
	doi = {10.3997/2214-4609.202210531},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/louboutin2022eageewi/louboutinp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2022/louboutin2022eageewi/Jun-9-3-15-mloubout.pdf},
	software = {https://github.com/slimgroup/TimeProbeSeismic.jl},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@conference {kumar2021SEGeuq,
	title = {Enabling uncertainty quantification for seismic data pre-processing using normalizing flows (NF){\textemdash}an interpolation example},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2021},
	note = {(IMAGE, Denver)},
	month = {09},
	pages = {1515-1519},
	abstract = {Seismic data go through a sequence of pre-processing steps before being made into an image. Although some work has been done to assess the uncertainties in the final images, how the uncertainty in the pre-processing affects the results remains largely unexplored. We use Normalizing Flows (NF), a type of deep neural network, to interpolate seismic data and quantify the associated uncertainty. A big advantage of NFs, over the more commonly used Markov Chain Monte Carlo methods, is that they can successfully sample a complex and high-dimensional probability space with fewer assumptions. We present the first application of NF in interpolating (synthetic) seismic data. The statistical measurements retrieved from the network can be used to better characterize the data as it is passed to the post-processing phase.},
	keywords = {deep learning, normalizing flow, SEG, wavefield reconstruction},
	doi = {10.1190/segam2021-3583705.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2021/kumar2021SEGeuq/kumar2021SEGeuq.pdf},
	software = {https://github.com/slimgroup/Software.SEG2021},
	author = {Rajiv Kumar and Maria Kotsi and Ali Siahkoohi and Alison Malcolm}
}
@conference {yin2020SEGesi,
	title = {Extended source imaging {\textendash}- a unifying framework for seismic and medical imaging},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2020},
	note = {(SEG, virtual)},
	month = {09},
	pages = {3502-3506},
	abstract = {We present three imaging modalities that live on the crossroads of seismic and medical imaging. Through the lens of extended source imaging, we can draw deep connections among the fields of wave-equation based seismic and medical imaging, despite first appearances. From the seismic perspective, we underline the importance to work with the correct physics and spatially varying velocity fields. Medical imaging, on the other hand, opens the possibility for new imaging modalities where outside stimuli, such as laser or radar pulses, can not only be used to identify endogenous optical or thermal contrasts but that these sources can also be used to insonify the medium so that images of the whole specimen can in principle be created.},
	keywords = {medical imaging, SEG, seismic imaging, variable projection},
	doi = {10.1190/segam2020-3426999.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/yin2020SEGesi/yin2020SEGesi_pres.mp4},
	software = {https://github.com/slimgroup/Software.SEG2020},
	author = {Ziyi Yin and Rafael Orozco and Philipp A. Witte and Mathias Louboutin and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {witte2019SEGedw,
	title = {Event-driven workflows for large-scale seismic imaging in the cloud},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {3984-3988},
	abstract = {Cloud computing has seen a large rise in popularity in recent years and is becoming a cost effective alternative to on-premise computing, with theoretically unlimited scalability. However, so far little progress has been made in adapting the cloud for high performance computing (HPC) tasks, such as seismic imaging and inversion. As the cloud does not provide the same type of fast and reliable connections as conventional HPC clusters, porting legacy codes developed for HPC environments to the cloud is ineffective and misses out on an opportunity to take advantage of new technologies presented by the cloud. We present a novel approach of bringing seismic imaging and inversion workflows to the cloud, which does not rely on a traditional HPC environment, but is based on serverless and event-driven computations. Computational resources are assigned dynamically in response to events, thus minimizing idle time and providing resilience to hardware failures. We test our workflow on two large-scale imaging examples and demonstrate that cost-effective HPC in the cloud is possible, but requires careful reconsiderations of how to bring software to the cloud.},
	keywords = {cloud, Imaging, large-scale, LS-RTM, RTM, SEG, workflow},
	doi = {10.1190/segam2019-3215069.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/witte2019SEGedw/witte2019SEGedw_pres.pdf},
	author = {Philipp A. Witte and Mathias Louboutin and Henryk Modzelewski and Charles Jones and James Selvage and Felix J. Herrmann}
}
@conference {louboutin2018SEGeow,
	title = {Effects of wrong adjoints for RTM in TTI media},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {331-335},
	abstract = {In order to obtain accurate images of the subsurface, anisotropic modeling and imaging is necessary. However, the twenty-one parameter complete wave-equation is too computationally expensive to be of use in this case. The transverse tilted isotropic wave-equation is then the next best feasible representation of the physics to use for imaging. The main complexity arising from transverse tilted isotropic imaging is to model the receiver wavefield (back propagation of the data or data residual) for the imaging condition. Unlike the isotropic or the full physics wave-equations, the transverse tilted isotropic wave-equation is not not self-adjoint. This difference means that time-reversal will not model the correct receiver wavefield and this can lead to incorrect subsurface images. In this work, we derive and implement the adjoint wave-equation to demonstrate the necessity of exact adjoint modeling for anisotropic modeling and compare our result with adjoint-free time-reversed imaging.},
	keywords = {Adjoint, anisotropy, finite-differences, Imaging, RTM, SEG, TTI},
	doi = {10.1190/segam2018-2996274.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/louboutin2018SEGeow/louboutin2018SEGeow.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/louboutin2018SEGeow/louboutin2018SEGeow_pres.pdf},
	author = {Mathias Louboutin and Philipp A. Witte and Felix J. Herrmann}
}
@conference {fang2017AIPepm,
	title = {An efficient penalty method for PDE-constrained optimization problem with source estimation and stochastic optimization},
	booktitle = {Applied Inverse Problems Annual Conference Proceedings},
	year = {2017},
	note = {(AIP, Hangzhou)},
	month = {05-06},
	pages = {40},
	abstract = {Many inverse problems in applied science and engineering can be written as PDE-constrained optimization problems. Typically, one is interested in estimating the unknown medium parameters of the PDE describing the system, given multi-source measurements of the fields in the true medium. The objective function is often very oscillatory, which gives rise to problematic local minima, and merely evaluating one or more of its derivatives requires solving multiple PDEs, which are expensive. In many applications such as seismic exploration, only the source location is available and the source signature itself is unknown, which results in erroneous estimated parameters. In this work, we propose a penalty method to solve the PDE-constrained optimization problem, whereby we replace the PDE constraints by a two-norm penalty term, which leads to a bi-linear optimization problem with a much less oscillatory objective function. By applying the variable projection method, we develop an efficient method to invert the unknown parameters and source signatures simultaneously. To reduce the computational cost, we randomly select a subset of all data to update the parameters at each iteration. Numerical examples demonstrate that this method is less prone to becoming stuck in harmful local minima and is able to successfully invert the unknown parameters and source signature with less computational cost compared to using all the data.},
	keywords = {Optimization, penalty method, source estimation, Stochastic optimization},
	url = {http://aip2017.csp.escience.cn/dct/page/1},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AIP/2017/fang2017AIPepm/fang2017AIPepm_pres.pdf},
	author = {Zhilong Fang and Curt Da Silva and Felix J. Herrmann}
}
@conference {louboutin2017EAGEess,
	title = {Extending the search space of time-domain adjoint-state FWI with randomized implicit time shifts},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Adjoint-state full-waveform inversion aims to obtain subsurface properties such as velocity, density or anisotropy parameters, from surface recorded data. As with any (non-stochastic) gradient based optimization procedure, the solution of this inversion procedure is to a large extend determined by the quality of the starting model. If this starting model is too far from the true model, these derivative-based optimizations will likely end up in local minima and erroneous inversion results. In certain cases, extension of the search space, e.g. by making the wavefields or focused matched sources additional unknowns, has removed some of these non-uniqueness issues but these rely on time-harmonic formulations. Here, we follow a different approach by combining an implicit extension of the velocity model, time compression techniques and recent results on stochastic sampling in non-smooth/non-convex optimization},
	keywords = {cycle skipping, EAGE, FWI, inversion, nonconvex, time domain},
	doi = {10.3997/2214-4609.201700831},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEess/louboutin2017EAGEess.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEess/louboutin2017EAGEess_pres.pdf},
	author = {Mathias Louboutin and Felix J. Herrmann}
}
@conference {kumar2014EAGEeia,
	title = {Extended images in action: efficient WEMVA via randomized probing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. In this paper, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points into a matrix whose (i,j)^th entry captures the interaction between gridpoints i and j. For even small problems, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via efficient matrix-vector products. We illustrate how this can be used to construct objective functions for automatic MVA.},
	keywords = {EAGE, extended imaging, MVA, probing},
	doi = {10.3997/2214-4609.20141492},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/kumar2014EAGEeia/kumar2014EAGEeia_pres.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {kumar2013SEGMVA,
	title = {Efficient WEMVA using extended images},
	booktitle = {SEG Workshop on Advances in Model Building, Imaging, and FWI; Houston},
	year = {2013},
	month = {09},
	abstract = {Image gathers as a function of subsurface offset are an important tool for velocity analysis in areas of complex geology. Here, we offer a new perspective on image gathers by organizing the extended image as a function of all subsurface offsets and all subsurface points in to a matrix whose (i,j)th entry captures the interaction between gridpoints i and j. For even small problems, it is infeasible to form and store this matrix. Instead, we propose an efficient algorithm to glean information from the image volume via efficient matrix-vector products. We illustrate how this can be used to construct objective functions for automated MVA.},
	keywords = {inversion, MVA, poster, SEG, workshop},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/kumar2013SEGMVA/kumar2013SEGMVA_poster.pdf},
	author = {Rajiv Kumar and Tristan van Leeuwen and Felix J. Herrmann}
}
@conference {li2011CSEGefimag,
	title = {Efficient full-waveform inversion with marine acquisition geometry},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2012},
	month = {02},
	abstract = {Full-waveform inversion (FWI) is a nonlinear data fitting procedure based on seismic data to derive a accurate velocity model. With the increasing demand for high resolution images in complex geological settings, the importance of improvements in acquisition and inversion become more and more critical. However, these improvements will be obtained at high computational cost, as a typical marine survey contains thousands of shot and receiver positions, and FWI needs several passes through massive seismic data. Computational cost of FWI will grow exponentially as the size of seismic data and desired resolution increase. In this paper we present a modified Gauss-Newton (GN) method that borrows ideas from compressive sensing, where we compute the GN updates from a few randomly selected sequential shots. Each subproblem is solved by using a sparsity promoting algorithm. With this approach, we dramatically reduce the size and hence the computational costs of the problem, whilst we control information loss by redrawing a different set of sequential shots for each subproblem.},
	keywords = {CSEG},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2012/li2011CSEGefimag/li2011CSEGefimag.pdf},
	author = {Xiang Li and Felix J. Herrmann}
}
@conference {herrmann2011EAGEefmsp,
	title = {Efficient least-squares migration with sparsity promotion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {Seismic imaging relies on the collection of multi-experimental data volumes in combination with a sophisticated back-end to create high-fidelity inversion results. While significant improvements have been made in linearized inversion, the current trend of incessantly pushing for higher quality models in increasingly complicated regions reveals fundamental shortcomings in handling increasing problem sizes numerically. The so-called {\textquoteleft}{\textquoteleft}curse of dimensionality" is the main culprit because it leads to an exponential growth in the number of sources and the corresponding number of wavefield simulations required by {\textquoteleft}{\textquoteleft}wave-equation" migration. We address this issue by reducing the number of sources by a randomized dimensionality reduction technique that combines recent developments in stochastic optimization and compressive sensing. As a result, we replace the cur- rent formulations of imaging that rely on all data by a sequence of smaller imaging problems that use the output of the previous inversion as input for the next. Empirically, we find speedups of at least one order-of-magnitude when each reduced experiment is considered theoretically as a separate compressive-sensing experiment.},
	keywords = {EAGE, Imaging, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/herrmann11EAGEefmsp/herrmann11EAGEefmsp_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50333},
	author = {Felix J. Herrmann and Xiang Li}
}
@conference {lin2011EAGEepsic,
	title = {Estimating primaries by sparse inversion in a curvelet-like representation domain},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2011},
	month = {05},
	abstract = {We present an uplift in the fidelity and wavefront continuity of results obtained from the Estimation of Primaries by Sparse Inversion (EPSI) program by reconstructing the primary events in a hybrid wavelet-curvelet representation domain. EPSI is a multiple removal technique that belongs to the class of wavefield inversion methods, as an alternative to the traditional adaptive-subtraction process. The main assumption is that the correct primary events should be as sparsely-populated in time as possible. A convex reformulation of the original EPSI algorithm allows its convergence property to be preserved even when the solution wavefield is not formed in the physical domain. Since wavefronts and edge-type singularities are sparsely represented in the curvelet domain, sparse solutions formed in this domain will exhibit vastly improved continuity when compared to those formed in the physical domain, especially for the low-energy events at later arrival times. Further- more, a wavelet-type representation domain will preserve sparsity in the reflected events even if they originate from non-zero-order discontinuities in the subsurface, providing an additional level of robustness. This method does not require any changes in the underlying computational algorithm and does not explicitly impose continuity constraints on each update.},
	keywords = {EAGE, Presentation, Processing},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2011/lin11EAGEepsic/lin11EAGEepsic_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=50427},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {johnson2010EAGEeop,
	title = {Estimation of primaries via sparse inversion with reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	abstract = {Accurate removal of surface related multiples is a key step in seismic data processing. The industry standard for removing multiples is SRME, which involves convolving the data with itself to predict the multiples, followed by an adaptive subtraction procedure to recover the primaries (Verschuur and Berkhout, 1997). Other methods involve multidimensional division of the up-going and down-going wavefields (Amundsen, 2001). However, this approach may suffer from stability problems. With the introduction of the {\textquoteleft}{\textquoteleft}estimation of primaries by sparse inversion{\textquoteright}{\textquoteright}(EPSI), van Groenestijn and Verschuur (2009) recentely reformulated SRME to jointly estimate the surface-free impulse response and the source signature directly from the data. The advantage of EPSI is that it recovers the primary response directly, and does not require a second processing step for the subtraction of estimated multiples from the original data. However, because it estimates both the primary impulse response and source signature from the data EPSI must be regularized. Motivated by recent successful application of the curvelet transform in seismic data processing (Herrmann et al., 2007), we formulate EPSI as a bi-convex optimization problem that seeks sparsity on the surface-free Green{\textquoteright}s function and Fourier-domain smoothness on the source wavelet. Our main contribution compared to previous work (Lin and Herrmann, 2009), and the contribution of that author to the proceedings of this meeting(Lin and Herrmann, 2010), is that we employ the physical principle of as source-receiver reciprocity to improve the inversion.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/johnson10EAGEeop/johnson10EAGEeop.pdf},
	author = {James Johnson and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {vandenberg2008IAMesr,
	title = {Exact sparse reconstruction and neighbourly polytopes},
	booktitle = {IAM},
	year = {2008},
	keywords = {IAM, Presentation, private, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Private/Conferences/IAM/2008/vandenberg2008IAMesr/vandenberg2008IAMesr.pdf},
	author = {Ewout van den Berg}
}
