@conference {deng2025IMAGEtfi,
	title = {Time-lapse full-waveform inversion with uncertainty quantification},
	booktitle = {International Meeting for Applied Geoscience and Energy},
	year = {2025},
	month = {8},
	keywords = {Amortized Variational Inference, Bayesian inference, deep learning, FWI, GCS, igm, IMAGE, image generative model, Imaging, Inverse problems, JRM, SEG, Summary Statistics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2025/deng2025IMAGEtfi/IMAGE_Venice.pdf},
	author = {Zijun Deng and Rafael Orozco and Abhinav Prakash Gahlot and Felix J. Herrmann}
}
@conference {rizzuti2019SEGadf,
	title = {A dual formulation for time-domain wavefield reconstruction inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {1480-1485},
	abstract = {We illustrate a dual formulation for full-waveform inversion potentially apt to large 3-D problems. It is based on the optimization of the wave equation compliance, under the constraint of data misfit not exceeding a prescribed noise level. In the Lagrangian formulation, model and wavefield state variables are complemented with multipliers having the same dimension of data ("dual data" variables). Analogously to classical wavefield reconstruction inversion, the wavefield unknowns can be projected out in closed form, by solving a version of the augmented wave equation. This leads to a saddle-point problem whose variables are only model and dual data. As such, this formulation represents a model extension, and is potentially robust against local minima. The classical downsides of model extension methods and wavefield reconstruction inversion are here effectively mitigated: storage of the dual variables is affordable, the augmented wave equation is amenable to time-marching finite-difference schemes, and no continuation strategy for penalty parameters is needed, with the prospect of 3-D applications.},
	keywords = {3D, Full-waveform inversion, SEG, Time-domain},
	doi = {10.1190/segam2019-3216760.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/rizzuti2019SEGadf/rizzuti2019SEGadf.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/rizzuti2019SEGadf/rizzuti2019SEGadf_pres.pdf},
	software = {https://github.com/slimgroup/Software.rizzuti2019SEGadf},
	author = {Gabrio Rizzuti and Mathias Louboutin and Rongrong Wang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@conference {daskalakis2017SEGdds,
	title = {Developments in the direction of solving extremly large problems in Geophysics},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {4375-4378},
	abstract = {Often in exploration Geophysics we are forced to work with extremely large problems. Acquisitions via dense grids of receivers translate into very large mathematical systems. Usually, depending on the acquisition, the size of the matrix of the system to be solved, can be measured in the millions. The proper way to address this problem is by subsampling. Even though subsampling can reduce the computational efforts required, it can not address stability problems caused by preconditioning and/or instrumental response errors. In this abstract, we introduce a modification of the linearized Bregman solver for these large problems that resolves stability issues.},
	keywords = {cycling, least-squares migration, linearized Bregman, SEG, weighted increment},
	doi = {10.1190/segam2017-17795188.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/daskalakis2017SEGdds/daskalakis2017SEGdds.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/daskalakis2017SEGdds/daskalakis2017SEGdds_pres.pdf},
	author = {Emmanouil Daskalakis and Rachel Kuske and Felix J. Herrmann}
}
@conference {yang2017SEGfsp,
	title = {Fast sparsity-promoting least-squares migration with multiples in time domain},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {4828-4832},
	abstract = {Based on the latest developments of research in inversion technology with optimization, researchers have made significant progress in the implementation of least-squares reverse-time migration (LS-RTM) of primaries. In Marine data however, these applications rely on the success of a pre-imaging separation of primaries and multiples, which can be modeled as a multi-dimensional convolution between the vertical derivative of the surface-free Green{\textquoteright}s function and the down-going receiver wavefield. Instead of imaging the primaries and multiples separately, we implement the LS-RTM of the total down-going wavefield by combining areal source injection and linearized Born modelling, where strong surface related multiples are generated from a strong density variation at the ocean bottom. The advantage including surface related multiples in LS-RTM is the extra illumination we obtain from these multiples without incurring additional computational costs related to carrying out multi-dimensional convolutions part of conventional multiple prediction procedures. Even though we are able to avert these computational costs, our approach shares the large costs of LSRTM. We reduce these costs by combining randomized source subsampling with our sparsity-promoting imaging technology, which produces artifact-free, high-resolution images, with the surface-related multiples migrated properly.},
	keywords = {least-squares RTM, multiples, SEG, sparsity, time domain},
	doi = {10.1190/segam2017-17741608.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGfsp/yang2017SEGfsp.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/yang2017SEGfsp/yang2017SEGfsp_pres.pdf},
	author = {Mengmeng Yang and Emmanouil Daskalakis and Felix J. Herrmann}
}
@conference {zhang2017SEGmsd,
	title = {Massive 3D seismic data compression and inversion with hierarchical Tucker},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {1347-1352},
	abstract = {Modern-day oil and gas exploration, especially in areas of complex geology such as fault belts and sub-salt areas, is an increasingly expensive and risky endeavour. Typically long-offset and dense sampling seismic data are required for subsequent shot based processing procedures, e.g. wave-equation based inversion (WEI) and surface-related multiple elimination (SRME). However, these strict requirements result in an exponential growth in data volume size and prohibitive demands on computational resources, given the multidimensional nature of the data volumes. Moreover the physical constraints and cost limitations impose restrictions on acquiring fully sampled data. In this work, we propose to invert our large-scale data from a set of subsampled measurements, resulting in an estimate of the true volume in a compressed low-rank tensor format. Rather than expanding the data to its fully-sampled form for later downstream processes, we demonstrate how to use this compressed data directly via on-the-fly common shot or receiver gathers extraction. The combination of massive compression and fast on demand data reconstruction of 3D shot or receiver gathers leads to a substantial reduction in memory costs but with minimal effects on results in the subsequent processing procedures. We demonstrate the effective implementation of our proposed framework on full-waveform inversion on a 3D seismic synthetic data set generated from a Overthrust model.},
	keywords = {3D, FWI, SEG, tensor algebra},
	doi = {10.1190/segam2017-17742951.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/zhang2017SEGmsd/zhang2017SEGmsd_pres.pdf},
	author = {Yiming Zhang and Curt Da Silva and Rajiv Kumar 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 {lopez2015SEGWSsfm,
	title = {SVD-free matrix completion in randomized marine acquisition},
	booktitle = {SEG Workshop on Rank-Reduction and Other Sparse Transform Methods with Application to Data Reconstruction, De-Noising, De-Blending and Imaging; New Orleans},
	year = {2015},
	note = {(SEG, New Orleans)},
	month = {10},
	abstract = {During this presentation, we will illustrate how insights from matrix-free matrix completion can be used to solve problems in (on- and off-the-grid) missing trace interpolation and time-jittered (simultaneous) marine acquisition. In particular, we will demonstrate the importance of data organization to favor recovery by rank minimization and the use of deterministic spectral properties to predict the recovery. We also make make comparisons between wavefield reconstructions based on transform-domain sparsity promotion and matrix completion.},
	keywords = {Acquisition, interpolation, matrix completion, rank reduction, SEG, workshop},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2015/lopez2015SEGWSsfm/lopez2015SEGWSsfm_pres.pdf},
	author = {Oscar Lopez and Haneet Wason and Curt Da Silva and Rajiv Kumar and Felix J. Herrmann}
}
@conference {dasilva2013SEGhtuck,
	title = {Structured tensor missing-trace interpolation in the Hierarchical Tucker format},
	booktitle = {SEG Technical Program Expanded Abstracts},
	volume = {32},
	year = {2013},
	month = {09},
	pages = {3623-3627},
	publisher = {SEG},
	organization = {SEG},
	abstract = {Owing to the large scale and dimensionality of a 3D seismic experiment, acquiring fully-sampled data according to the Nyquist criterion is an exceedingly arduous and cost-prohibitive task. In this paper, we develop tools to interpolate 5D seismic volumes with randomly missing sources or receivers using a relatively novel tensor format known as the Hierarchical Tucker (HT) format. By exploiting the underlying smooth structure of HT tensors, specifically its smooth manifold structure, we develop solvers which are fast, immediately parallelizable, and SVD-free, making these solvers amenable to large-scale problems where SVD-based projection methods are far too costly. We also build on intuition of multidimensional sampling from the perspective of matrix-completion and demonstrate the ability of our algorithms to recover frequency slices even amidst very high levels of source subsampling on a synthetic large-scale 3D North Sea dataset.},
	keywords = {differential geometry, gauss newton, hierarchical tucker, riemannian optimization, SEG, structured tensor, tensor interpolation},
	doi = {10.1190/segam2013-0709.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/dasilva2013SEGhtuck/dasilva2013SEGhtuck.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2013/dasilva2013SEGhtuck/dasilva2013SEGhtuck_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
