@conference {alfaraj2018EAGEasa,
	title = {Automatic statics and residual statics correction with low-rank approximation},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2018},
	note = {(EAGE, Copenhagen)},
	month = {06},
	abstract = {Seismic data, mainly on land, suffers from long-wavelength statics due to the laterally varying and heterogeneous nature of the near-surface weathering layers. We propose an automatic, data-driven and computationally efficient statics correction method based on low-rank approximation to correct for such statics. The method does not require a model to estimate static time shifts, which is the case for other static correction methods; rather it applies the appropriate static corrections on the data such that it becomes low rank in a certain domain. As of now, the method is applicable to data that has been corrected for elevation statics. Due to the near-surface irregularities and due to approximations used by static correction methods that lead to not fully correcting for statics, an iterative residual statics correction becomes necessary. Our proposed method corrects for residual statics without the necessity of the surface consistency assumption and a multi-iterate process. Additional benefits of the method include artifacts and noise suppression. We demonstrate the successful application of our method on several synthetic data examples.},
	keywords = {EAGE, land, Rank, residual statics, statics},
	doi = {10.3997/2214-4609.201801107},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2018/alfaraj2018EAGEasa/alfaraj2018EAGEasa_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2018SEGswi,
	title = {Seismic waveform inversion using decomposed one-way wavefields},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2018},
	note = {(SEG, Anaheim)},
	month = {10},
	pages = {1379-1383},
	abstract = {Conventional seismic full waveform inversion (FWI) estimates a velocity model by matching the two-way predicted data with the observed data. The non-linearity of the problem and the complexity of the Earth{\textquoteright}s subsurface, which results in complex wave propagation may lead to unsatisfactory inversion results. Moreover, having inaccurate overburden models, i.e water velocity model in the ocean bottom acquisition setting, may lead to erroneous velocity models. In order to obtain better velocity models, we simplify the FWI problem by decomposing the total two-way observed and predicted wavefields into one-way wavefields at the receiver locations using acoustic wavefield decomposition. We then propose to fit the less-complex one-way wavefield to obtain a velocity model that contains valuable information about the overburden. We use this inverted velocity model as a better initial model for the inversion using the other more-complex one-way wavefield. We demonstrate our proposed algorithm on acoustic non-inverse crime synthetic data produced from the Marmousi2 model. The proposed method provides improved inversion results compared with conventional FWI due to properly accounting for separate model updates from the up- and down-going wavefields.},
	keywords = {decomposition, FWI, one-way, SEG},
	doi = {10.1190/segam2018-2998162.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/alfaraj2018SEGswi/alfaraj2018SEGswi.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2018/alfaraj2018SEGswi/alfaraj2018SEGswi_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@presentation {alfaraj2017SINBADFros,
	title = {Reconstruction of S-waves from low-cost randomized acquisition},
	year = {2017},
	publisher = {SINBAD},
	abstract = {Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier according to the Nyquist sampling criterion. To avoid higher acquisition costs and to utilize the multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. We present two up- and down-going shear wave reconstruction methods: (i) rank minimization reconstruction followed by elastic wavefield decomposition, and (ii) sparsity promoting joint interpolation decomposition using all the multicomponent data in one optimization problem.},
	keywords = {Presentation, SINBAD, SINBADFALL2017, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2017/Fall/alfaraj2017SINBADFros/alfaraj2017SINBADFros.mov},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2017SEGrsw,
	title = {Reconstruction of S-waves from low-cost randomized and simultaneous acquisition by joint sparse inversion},
	booktitle = {SEG Technical Program Expanded Abstracts},
	year = {2017},
	note = {(SEG, Houston)},
	month = {09},
	pages = {2533-2538},
	abstract = {Spatial Nyquist sampling rate, which is proportional to the apparent subsurface velocity, is the key deciding cost factor for conventional seismic acquisition. Due to the lower shear wave velocity compared with compressional waves, finer spatial sampling is required to properly record the earlier, which increases the acquisition costs. This is one of the reasons why shear waves are usually not considered in practice. To avoid having the Nyquist criterion as the deciding cost factor and to utilize multicomponent data to its available full extent, we propose acquiring randomly undersampled ocean bottom seismic data. Each component can then be interpolated separately, followed by elastic decomposition to recover up- and down-going S-waves. Instead, we jointly interpolate and decompose the recorded multicomponent data by solving one sparsity promoting optimization problem. This way we ensure that the relative amplitudes of the multicomponent data is preserved. We compare two sparsifying transforms: the curvelet transform and the frequency-wavenumber transform. Another key cost deciding factor for seismic acquisition is the efficiency of acquiring data. This calls for simultaneous acquisition, which requires a source separation step. Similarly, instead of taking a two-step approach, we perform a sparsity-promoting joint source separation decomposition. Results on economically and efficiently acquired synthetic data of both joint methods show their ability of reconstructing accurate up- and down-going S-waves.},
	keywords = {decomposition, elastic, interpolation, randomized acquisition, SEG, shear wave, source separation, sparse},
	doi = {10.1190/segam2017-17681376.1},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw.html},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SEG/2017/alfaraj2017SEGrsw/alfaraj2017SEGrsw_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
@conference {alfaraj2017EAGEswr,
	title = {Shear wave reconstruction from low cost randomized acquisition},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2017},
	note = {(EAGE, Paris)},
	month = {06},
	abstract = {Shear waves travel in the subsurface at a lower speed compared with compressional waves. Therefore, much finer spatial sampling is required to properly record the shear waves. This leads to higher acquisition costs which are typically avoided by designing surveys geared towards only compressional waves imaging. We propose using randomly under-sampled ocean bottom acquisition for recording both compressional and shear waves. The recorded multicomponent data is then interpolated using an SVD-free low rank interpolation scheme that is feasible for large scale seismic data volumes to obtain finely sampled data. Following that, we perform elastic wavefield decomposition at the ocean bottom to recover accurate up- and dow-going S-waves. Synthetic data results indicate that using randomized under-sampled acquisition, we can recover accurate S-waves with an economical cost compared with conventional acquisition designs.},
	keywords = {EAGE, interpolation, randomized acquisition, rank minimization, shear waves},
	doi = {10.3997/2214-4609.201700594},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/alfaraj2017EAGEswr/alfaraj2017EAGEswr.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2017/alfaraj2017EAGEswr/alfaraj2017EAGEswr_pres.pdf},
	author = {Ali M. Alfaraj and Rajiv Kumar and Felix J. Herrmann}
}
