@conference {dasilva2015EAGEogt,
	title = {Off the grid tensor completion for seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2015},
	note = {(EAGE, Madrid)},
	month = {06},
	abstract = {The practical realities of acquiring seismic data in a realistic survey are often at odds with the stringent requirements of Shannon-Nyquist-based sampling theory. The unpredictable movement of the ocean{\textquoteleft}s currents can be detrimental in acquiring exactly equally-spaced samples while sampling at Nyquist-rates are expensive, given the huge dimensionality and size of the data volume. Recent work in matrix and tensor completion for seismic data interpolation aim to alleviate such stringent Nyquist-based sampling requirements but are fundamentally posed on a regularly-spaced grid. In this work, we extend our previous results in using the so-called Hierarchical Tucker (HT) tensor format for recovering seismic data to the irregularly sampled case. We introduce an interpolation operator that resamples our tensor from a regular grid (in which we impose our low-rank constraints) to our irregular sampling grid. Our framework is very flexible and efficient, depending primarily on the computational costs of this operator. We demonstrate the superiority of this approach on a realistic BG data set compared to using low-rank tensor methods that merely use binning.},
	keywords = {EAGE, hierarchical tucker, irregular sampling, off the grid, structured tensor, tensor interpolation},
	doi = {10.3997/2214-4609.201412978},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/dasilva2015EAGEogt/dasilva2015EAGEogt.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2015/dasilva2015EAGEogt/dasilva2015EAGEogt_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 {fang2014EAGEfuq,
	title = {Fast uncertainty quantification for 2D full-waveform inversion with randomized source subsampling},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {Uncertainties arise in every area of seismic exploration, especially in full-waveform inversion, which is highly non-linear. In the framework of Bayesian inference, uncertainties can be analyzed by sampling the posterior probability density distribution with a Markov chain Monte-Carlo (McMC) method. We reduce the cost of computing the posterior distribution by working with randomized subsets of sources. These approximations, together with the Gaussian assumption and approximation of the Hessian, leads to a computational tractable uncertainty quantification. Application of this approach to a synthetic leads to standard deviations and confidence intervals that are qualitatively consistent with our expectations.},
	keywords = {EAGE, FWI, Markov chain Monte Carlo, randomized sources subsampling, Uncertainty quantification},
	doi = {10.3997/2214-4609.20140715},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/fang2014EAGEfuq/fang2014EAGEfuq.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/fang2014EAGEfuq/fang2014EAGEfuq_pres.pdf},
	author = {Zhilong Fang and Curt Da Silva and Felix J. Herrmann}
}
@conference {dasilva2014EAGEhtucknoisy,
	title = {Low-rank promoting transformations and tensor interpolation - applications to seismic data denoising},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In this abstract, we extend our previous work in Hierarchical Tucker (HT) tensor completion, which uses an extremely efficient representation for representing high-dimensional tensors exhibit- ing low-rank structure, to handle subsampled tensors with noisy entries. We consider a "low-noise" case, so that the energies of the noise and the signal are nearly indistinguishable, and a {\textquoteright}high-noise{\textquoteright} case, in which the noise energy is now scaled to the amplitude of the entire data volume. We examine the effect of the noise in terms of the singular values along different matricizations of the data, i.e., reshaping of the tensor along different modes. By interpreting this effect in the context of tensor completion, we demonstrate the inefficacy of denoising by this method in the source-receiver do- main. In light of this observation, we transform the decimated, noisy data in to the midpoint-offset domain, which promotes low-rank behaviour in the signal and high-rank behaviour in the noise. This distinction between signal and noise allows low-rank interpolation to effectively denoise the signal with only a marginal increase in computational cost. We demonstrate the effectiveness of this approach on a 4D frequency slice.},
	keywords = {EAGE, hierarchical tucker, low-rank transform, riemannian optimization, seismic denoising, structured tensor, tensor interpolation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/dasilva2014EAGEhtucknoisy/dasilva2014EAGEhtucknoisy.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/dasilva2014EAGEhtucknoisy/dasilva2014EAGEhtucknoisy_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@conference {dasilva2013EAGEhtucktensor,
	title = {Hierarchical Tucker tensor optimization - applications to 4D seismic data interpolation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2013},
	month = {06},
	abstract = {In this work, we develop optimization algorithms on the manifold of Hierarchical Tucker (HT) tensors, an extremely efficient format for representing high-dimensional tensors exhibiting particular low-rank structure. With some minor alterations to existing theoretical developments, we develop an optimization framework based on the geometric understanding of HT tensors as a smooth manifold, a generalization of smooth curves/surfaces. Building on the existing research of solving optimization problems on smooth manifolds, we develop Steepest Descent and Conjugate Gradient methods for HT tensors. The resulting algorithms converge quickly, are immediately parallelizable, and do not require the computation of SVDs. We also extend ideas about favourable sampling conditions for missing-data recovery from the field of Matrix Completion to Tensor Completion and demonstrate how the organization of data can affect the success of recovery. As a result, if one has data with randomly missing source pairs, using these ideas, coupled with an efficient solver, one can interpolate large-scale seismic data volumes with missing sources and/or receivers by exploiting the multidimensional dependencies in the data. We are able to recover data volumes amidst extremely high subsampling ratios (in some cases, > 75\%) using this approach.},
	keywords = {3D data interpolation, EAGE, riemannian optimization, structured tensor},
	doi = {10.3997/2214-4609.20130390},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/dasilva2013EAGEhtucktensor/dasilva2013EAGEhtucktensor.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2013/dasilva2013EAGEhtucktensor/dasilva2013EAGEhtucktensor_pres.pdf},
	author = {Curt Da Silva and Felix J. Herrmann}
}
@conference {dasilva2012EAGEprobingprecond,
	title = {Matrix probing and simultaneous sources: a new approach for preconditioning the Hessian},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2012},
	month = {06},
	abstract = {Recent advances based on the mathematical understanding of the Hessian as, under certain conditions, a pseudo-differential operator have resulted in a new preconditioner by L. Demanet et al. Basing their approach on a suitable basis expansion for the Hessian, by suitably {\textquoteright}probing{\textquoteright} the Hessian, i.e. applying the Hessian to a small number of randomized model perturbations, one can obtain an approximation to the inverse Hessian in an efficient manner. Building upon this approach, we consider this preconditioner in the context of least-squares migration and Full Waveform Inversion and specifically dimensionality reduction techniques in these domains. By utilizing previous work in simultaneous sources, we are able to develop an efficient least-squares migration scheme which recovers higher quality images and hence higher quality search directions in the context of a Gauss-Newton method for Full Waveform Inversion while simultaneously avoiding inordinate amounts of additional work.},
	keywords = {EAGE, matrix probing, pseudo-differential operator},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/dasilva2012EAGEprobingprecond/dasilva2012EAGEprobingprecond.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2012/dasilva2012EAGEprobingprecond/dasilva2012EAGEprobingprecond_pres.pdf},
	url2 = {http://earthdoc.eage.org/publication/publicationdetails/?publication=59193},
	author = {Curt Da Silva and Felix J. Herrmann}
}
