@conference {zhang2022SEGass,
	title = {A simulation-free seismic survey design by maximizing the spectral gap},
	booktitle = {International Meeting for Applied Geoscience and Energy Expanded Abstracts},
	year = {2022},
	note = {(IMAGE, Houston)},
	month = {05},
	abstract = {Due to the tremendous cost of seismic data acquisition, methods have been developed to reduce the amount of data acquired by designing optimal missing trace reconstruction algorithms. These technologies are designed to record as little data as possible in the field, while providing accurate wavefield reconstruction in the areas of the survey that are not recorded. This is achieved by designing randomized subsampling masks that allow for accurate wavefield reconstruction via matrix completion methods. Motivated by these recent results, we propose a simulation-free seismic survey design that aims at improving the quality of a given randomized subsampling using a simulated annealing algorithm that iteratively increases the spectral gap of the subsampling mask, a property recently linked to the quality of the reconstruction. We demonstrate that our proposed method improves the data reconstruction quality for a fixed subsampling rate on a realistic synthetic dataset.},
	keywords = {Acquisition, matrix factorization, SEG, spectral gap, survey design, wavefield reconstruction},
	doi = {10.1190/image2022-3751690.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/Yijun2022SEGass.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2022/zhang2022SEGass/index.html},
	software = {https://github.com/slimgroup/opt_spectral_gap},
	author = {Yijun Zhang and Mathias Louboutin and Ali Siahkoohi and Ziyi Yin and Rajiv Kumar and Felix J. Herrmann}
}
@conference {ren2021AGUsvi,
	title = {Seismic Velocity Inversion and Uncertainty Quantification Using Conditional Normalizing Flows},
	booktitle = {AGU Annual Meeting},
	year = {2021},
	note = {(AGU 2021, New Orleans)},
	month = {12},
	pages = {U12A-03},
	abstract = {In this work, we use a conditional normalizing flow (CNF) to address the seismic velocity inversion problem. Considering the large dimension difference between seismic data and velocity model, we reduce the data dimension by calculating its reverse time. After that, we train the CNF on pairs of migrated data and velocity. During inference, given a new seismic data, feeding the corresponding migrated image into the trained CNF will lead to posterior samples of the velocity inversion distribution. In addition, uncertainty quantification of the inverted results can be achieved by statistical metrics like mean and standard deviation. In our numerical example, the implementation is based on open-sourced software InvertibleNetworks.jl (Witte et al., 2021), JUDI.jl (Witte et al., 2019) and Devito (Louboutin et al., 2019). The training dataset are built based on the SEG/EAGE Overthrust model. For an unseen seismic data, the posterior samples of inversion results given by the trained CNF can be considered as good estimates of the true velocity. Especially, judging from the metrics like MAE, MSE, PSNR, SSIM, et al., the posterior mean is usually closer to the true velocity and the standard deviation indicates that the velocity value is more reliable within the subsurface layers than that on layer edges. Moreover, the inverted results, either the posterior samples or posterior mean, can be used as an initial model in the subsequent FWI for a more accurate result.},
	keywords = {AGU, inversion, machine learning, Normalizing flows, Uncertainty quantification},
	url = {https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/815883},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/AGU/2021/ren2021AGUsvi/ren2021AGUsvi_pres.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/AGU/2021/ren2021AGUsvi/ren2021AGUsvi.mp4},
	software = {https://github.com/slimgroup/INN_Velocity-Migration},
	author = {Yuxiao Ren and Philipp A. Witte and Ali Siahkoohi and Mathias Louboutin and Ziyi Yin and Felix J. Herrmann}
}
@conference {siahkoohi2020SIAMISsiu,
	title = {Seismic Imaging with Uncertainty Quantification: Sampling from the Posterior with Generative Networks},
	booktitle = {SIAM Conference on Imaging Science},
	year = {2020},
	note = {IS20},
	month = {7},
	abstract = {n/a},
	keywords = {Imaging, invertible networks, IS20, McMC, Uncertainty quantification},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SIAMIS/2020/siahkoohi2020SIAMISsiu/siahkoohi2020SIAMISsiu_pres.pdf},
	author = {Ali Siahkoohi and Philipp A. Witte and Mathias Louboutin and Felix J. Herrmann and Gabrio Rizzuti}
}
@conference {siahkoohi2019SEGsrm,
	title = {Surface-related multiple elimination with deep learning},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {4629-4634},
	abstract = {We explore the potential of neural networks in approximating the action of the computationally expensive Estimation of Primaries by Sparse Inversion (EPSI) algorithm, applied to real data, via a supervised learning algorithm. We show that given suitable training data, consisting of a relatively cheap prediction of multiples and pairs of shot records with and without surface-related multiples, obtained via EPSI, a well-trained neural network is capable of providing an approximation to the action of the EPSI algorithm. We perform our numerical experiment on the field Nelson data set. Our results demonstrate that the quality of the multiple elimination via our neural network improves compared to the case where we only feed the network with shot records with surface-related multiples. We take these benefits by supplying the neural network with a relatively poor prediction of the multiples, e.g. obtained by a relatively cheap single step of Surface-Related Multiple Elimination.},
	keywords = {EPSI, machine learning, SEG, SRME},
	doi = {10.1190/segam2019-3216723.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGsrm/siahkoohi2019SEGsrm.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGsrm/siahkoohi2019SEGsrm_pres.pdf},
	author = {Ali Siahkoohi and Dirk J. Verschuur and Felix J. Herrmann}
}
@conference {siahkoohi2018EAGEsdr,
	title = {Seismic data reconstruction with Generative Adversarial Networks},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {A main challenge in seismic imaging is acquiring densely sampled data. Compressed Sensing has provided theoretical foundations upon which desired sampling rate can be achieved by applying a sparsity promoting algorithm on sub-sampled data. The key point in successful recovery is to deploy a randomized sampling scheme. In this paper, we propose a novel deep learning-based method for fast and accurate reconstruction of heavily under-sampled seismic data, regardless of type of sampling. A neural network learns to do reconstruction directly from data via an adversarial process. Once trained, the reconstruction can be done by just feeding the frequency slice with missing data into the neural network. This adaptive nonlinear model makes the algorithm extremely flexible, applicable to data with arbitrarily type of sampling. With the assumption that we have access to training data, the quality of reconstructed slice is superior even for extremely low sampling rate (as low as 10\%) due to the data-driven nature of the method.},
	keywords = {data reconstruction, EAGE, generative adversarial networks, machine learning},
	doi = {10.3997/2214-4609.201801393},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/siahkoohi2018EAGEsdr/siahkoohi2018EAGEsdr.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/siahkoohi2018EAGEsdr/siahkoohi2018EAGEsdr_pres.pdf},
	author = {Ali Siahkoohi and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {siahkoohi2017SINBADFsdi,
	title = {Seismic data interpolation with Generative Adversarial Networks},
	year = {2017},
	publisher = {SINBAD},
	abstract = {In this project we implement an algorithm to predict the missing traces in the seismic shot gathers. The missing traces can be either regular or irregular. Any interpolation scheme assumes a prior knowledge on the data. Here the prior information used to interpolate the data is obtained from interaction of two trained deep neural networks, namely Generator and Discriminator. The combination of these two neural networks is called Generative Adversarial Network (GAN). GAN is trained on finely sampled seismic shot gathers. By employing the trained GAN we can project shot gathers with missing traces into the domain of the generator network. Then by computing the output of generator given the found projection, we can fill in the initial gather.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/siahkoohi2017SINBADFsdi/siahkoohi2017SINBADFsdi.mov},
	author = {Ali Siahkoohi and Felix J. Herrmann}
}
