@conference {siahkoohi2019SEGdlb,
	title = {Deep-learning based ocean bottom seismic wavefield recovery},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2019},
	note = {(SEG, San Antonio)},
	month = {09},
	pages = {2232-2237},
	abstract = {Ocean bottom surveys usually suffer from having very sparse receivers. Assuming a desirable source sampling, achievable by existing methods such as (simultaneous-source) randomized marine acquisition, we propose a deep-learning based scheme to bring the receivers to the same spatial grid as sources using a convolutional neural network. By exploiting source-receiver reciprocity, we construct training pairs by artificially subsampling the fully-sampled single-receiver frequency slices using a random training mask and later, we deploy the trained neural network to fill-in the gaps in single-source frequency slices. Our experiments show that a random training mask is essential for successful wavefield recovery, even when receivers are on a periodic gird. No external training data is required and experiments on a 3D synthetic data set demonstrate that we are able to recover receivers for up to 90 \% missing receivers, missing either randomly or periodically, with a better recovery for random case, at low to midrange frequencies.},
	keywords = {machine learning, obn, reciprocity, reconstruction, SEG},
	doi = {10.1190/segam2019-3216632.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGdlb/siahkoohi2019SEGdlb.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2019/siahkoohi2019SEGdlb/siahkoohi2019SEGdlb_pres.pdf},
	author = {Ali Siahkoohi and Rajiv Kumar and Felix J. Herrmann}
}
@conference {sharan2018SEGada,
	title = {A debiasing approach to microseismic},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {2942-2946},
	abstract = {Microseismic data is often used to locate fracture locations and their origin in time created by fracking. Although surface microseismic data can have large apertures and is easier to acquire than the borehole data, it often suffers from poor signal to noise ratio (S/R). Poor S/R poses a challenge in terms of estimating the correct location and source-time function of a microseismic source. In this work, we propose a denoising step in combination with a computationally cheap debiasing based approach to locate microseismic sources and to estimate their source-time functions with correct amplitude from extremely noisy data. Through numerical experiments, we demonstrate that our method can work with closely spaced microseismic sources with source-time functions of different peak amplitudes and frequencies. We have also shown the ability of our method with the smooth velocity model.},
	keywords = {Full-waveform inversion, Imaging, microseismic, noise, SEG, wave equation},
	doi = {10.1190/segam2018-2997252.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/sharan2018SEGada/sharan2018SEGada_pres.pdf},
	author = {Shashin Sharan and Rajiv Kumar and Rongrong Wang 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 {siahkoohi2018SEGcnn,
	title = {Deep Convolutional Neural Networks in prestack seismic{\textendash}-two exploratory examples},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {2196-2200},
	abstract = {Deep convolutional neural networks are having quite an impact and have resulted in step changes in the capabilities of image and voice recognition systems and there are indications they may have a similar impact on post-stack seismic images. While these developments are certainly important, we are after all dealing with an imaging problem involving an unknown earth and not completely understood physics. When the imaging step itself is not handled properly, this may possibly offset gains offered by deep learning. Motivated by our work on deep convolutional neural networks in seismic data reconstruction, we discuss how generative networks can be employed in prestack problems ranging from the relatively mondane removal of the effects of the free surface to dealing with the complex effects of numerical dispersion in time-domain finite differences. While the results we present are preliminary, there are strong indications that generative deep convolutional neural networks can have a major impact on complex tasks in prestack seismic data processing and modeling for inversion.},
	keywords = {Dispersion, machine learning, Modeling, Processing, SEG, SRME},
	doi = {10.1190/segam2018-2998599.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/siahkoohi2018SEGcnn/siahkoohi2018SEGcnn.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/siahkoohi2018SEGcnn/siahkoohi2018SEGcnn_pres.pdf},
	author = {Ali Siahkoohi and Mathias Louboutin and Rajiv Kumar 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}
}
