@conference {siahkoohi2020EAGEdlb,
	title = {A deep-learning based Bayesian approach to seismic imaging and uncertainty quantification},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2020},
	note = {Accepted in EAGE},
	month = {1},
	abstract = {Uncertainty quantification is essential when dealing with ill-conditioned inverse problems due to the inherent nonuniqueness of the solution. Bayesian approaches allow us to determine how likely an estimation of the unknown parameters is via formulating the posterior distribution. Unfortunately, it is often not possible to formulate a prior distribution that precisely encodes our prior knowledge about the unknown. Furthermore, adherence to handcrafted priors may greatly bias the outcome of the Bayesian analysis. To address this issue, we propose to use the functional form of a randomly initialized convolutional neural network as an implicit structured prior, which is shown to promote natural images and excludes images with unnatural noise. In order to incorporate the model uncertainty into the final estimate, we sample the posterior distribution using stochastic gradient Langevin dynamics and perform Bayesian model averaging on the obtained samples. Our synthetic numerical experiment verifies that deep priors combined with Bayesian model averaging are able to partially circumvent imaging artifacts and reduce the risk of overfitting in the presence of extreme noise. Finally, we present pointwise variance of the estimates as a measure of uncertainty, which coincides with regions that are difficult to image.},
	keywords = {deep learning, EAGE, seismic imaging, stochastic gradient Langevin dynamics, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2020/siahkoohi2020EAGEdlb/siahkoohi2020EAGEdlb.html},
	author = {Ali Siahkoohi and Gabrio Rizzuti and Felix J. Herrmann}
}
@conference {kadu2018EAGEdfwi,
	title = {Decentralized full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {With the advent of efficient seismic data acquisition, we are having a surplus of seismic data, which is improving the imaging of the earth using full-waveform inversion. However, such inversion suffers from many issues, including (i) substantial network waiting time due to repeated communications of function and gradient values in the distributed environment, and (ii) requirement of the sophisticated optimizer to solve an optimization problem involving non-smooth regularizers. To circumvent these issues, we propose a decentralized full-waveform inversion, a scheme where connected agents in a network optimize their objectives locally while being in consensus. The proposed formulation can be solved using the ADMM method efficiently. We demonstrate using the standard marmousi model that such scheme can decouple the regularization from data fitting and reduce the network waiting time.},
	keywords = {EAGE},
	doi = {10.3997/2214-4609.201801230},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/kadu2018EAGEdfwi/kadu2018EAGEdfwi.pdf},
	author = {Ajinkya Kadu and Rajiv Kumar}
}
@conference {louboutin2017EAGEdns,
	title = {Data normalization strategies for full-waveform inversion},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Amplitude mismatch is an inherent problem in seismic inversion. Most of the source estimation techniques are associated with amplitude uncertainty due to incomplete representation of the physics or estimation method parameters. Rewriting the inversion problem in an amplitude free formulation allows to mitigate the amplitude ambiguity and help the inversion process to converge. We present in this work two different strategies to lessen amplitude effects in seismic inversion, derive the corresponding update directions and show how we handle scaling error correctly in both the objective function and the gradient.},
	keywords = {EAGE, FWI, inversion, nonlinear, normalization},
	doi = {10.3997/2214-4609.201700720},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEdns/louboutin2017EAGEdns.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/louboutin2017EAGEdns/louboutin2017EAGEdns_poster.pdf},
	author = {Mathias Louboutin and Llu{\'\i}s Guasch and Felix J. Herrmann}
}
@conference {kumar2017EAGEdha,
	title = {Denoising high-amplitude cross-flow noise using curvelet-based stable principle component pursuit},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Removal of high-amplitude cross-flow noise in marine towed-streamer acquisition is of great interest because cross-flow noise hinders the success of subsequent processing (e.g. EPSI) and migration. However, the removal of cross-flow noise is a challenging process because cross-flow noise dominates steep angles and low-frequency components of the signal. As a result, applying a simple high-pass filter can result in a loss of coherent diving waves and reflected energy. We propose a stable curvelet-based principle-component pursuit approach that does not suffer from this shortcoming because it uses angle- and scale-adaptivity of the curvelet transform in combination with the low-rank property of cross-flow noise. As long as the cross-flow noise exhibits low-rank in the curvelet domain, our method successfully separates this signal component from the diving waves and seismic reflectivity, which is well-know to be sparse in the curvelet domain. Experimental results on a common-shot gather extracted from a coil shooting survey in the Gulf of Mexico shows the potential of our approach.},
	keywords = {coil data, cross-flow noise, curvelet, denoising, EAGE, SPCP},
	doi = {10.3997/2214-4609.201701055},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/kumar2017EAGEdha/kumar2017EAGEdha.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/kumar2017EAGEdha/kumar2017EAGEdha_poster.pdf},
	author = {Rajiv Kumar and Nick Moldoveanu and Felix J. Herrmann}
}
@conference {lin2009EAGEdsa,
	title = {Designing simultaneous acquisitions with compressive sensing},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2009},
	month = {06},
	abstract = {The goal of this paper is in designing a functional simultaneous acquisition scheme by applying the principles of compressive sensing. By framing the acquisition in a compressive sensing setting we immediately gain insight into not only how to choose the source signature and shot patterns, but also in how well we can hope to demultiplex the data when given a set amount of reduction in the number of sweeps. The principles of compressive sensing dictates that the quality of the demultiplexed data is closely related to the transform-domain sparsity of the solution. This means that, given an estimate in the complexity of the expectant data wavefield, it is possible to controllably reduce the number of shots that needs to be recorded in the field. We show a proof of concept by introducing an acquisition compatible with compressive sensing based on randomly phase-encoded vibroseis sweeps.},
	keywords = {EAGE, Presentation},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin09EAGEdsa.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2009/lin09EAGEdsa/lin2009EAGEdsa_pres.pdf},
	author = {Tim T.Y. Lin and Felix J. Herrmann}
}
