@conference {oghenekohwo2015IIPFWItlf,
	title = {Time-lapse FWI with distributed compressed sensing},
	booktitle = {Inaugural Full-Waveform Inversion Workshop},
	year = {2015},
	note = {(Natal, Brazil)},
	month = {08-09},
	abstract = {In this talk, I will discuss our most recent application of full-waveform inversion (FWI) to time-lapse seismic data inversion. Specifically, I will illustrate how a joint recovery model (JRM) can be used in a joint inversion framework, leveraging ideas from distributed compressed sensing (DCS). The key idea involves exploiting the common information in the baseline and monitor gradients during the inversion.},
	keywords = {distributed compressed sensing, FWI, time lapse},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/IIPFWI/oghenekohwo2015IIPFWItlf/oghenekohwo2015IIPFWItlf_pres.pdf},
	author = {Felix Oghenekohwo and Rajiv Kumar and Ernie Esser and Felix J. Herrmann}
}
@presentation {oghenekohwo2015SINBADFtli,
	title = {Time-lapse imaging with multiples and distributed compressed sensing},
	year = {2015},
	publisher = {SINBAD},
	abstract = {In this talk, we leverage our joint-recovery model, which uses the fact that time-lapse images generally have a lot in common, to improve imaging problems with large gaps. We demonstrate that the adverse effects of large acquisition gaps can be mitigated by using this joint-recovery model especially in combination with imaging with surface-related multiples.},
	keywords = {Presentation, SINBAD, SINBADFALL2015, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.pdf},
	url2 = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2015/Fall/oghenekohwo2015SINBADFtli/oghenekohwo2015SINBADFtli.mov},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
@conference {oghenekohwo2014EAGEtls,
	title = {Time-lapse seismic without repetition: reaping the benefits from randomized sampling and joint recovery},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2014},
	month = {06},
	abstract = {In the current paradigm of 4-D seismic, guaranteeing repeatability in acquisition and processing of the baseline and monitor surveys ranks highest amongst the technical challenges one faces in detecting time-lapse signals. By using recent insights from the field of compressive sensing, we show that the condition of survey repeatability can be relaxed as long as we carry out a sparsitypromoting program that exploits shared information between the baseline and monitor surveys. By inverting for the baseline and monitor survey as the common "background", we are able to compute high-fidelity 4-D differences from carefully selected synthetic surveys that have different sets of source/receivers missing. This synthetic example is proof of concept of an exciting new approach to randomized 4-D acquisition where time-lapse signal can be computed as long as the survey details, such as source/receiver locations are known afterwards.},
	keywords = {4-D seismic, EAGE, joint recovery, time-lapse},
	doi = {10.3997/2214-4609.20141478},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2014/oghenekohwo2014EAGEtls/oghenekohwo2014EAGEtls_pres.pdf},
	author = {Felix Oghenekohwo and Ernie Esser and Felix J. Herrmann}
}
@techreport {oghenekohwo2013SEGtlswrs,
	title = {Time-lapse seismics with randomized sampling},
	number = {TR-EOAS-2013-3},
	year = {2013},
	institution = {UBC},
	abstract = {In time-lapse or 4D seismics, repeatability of the acquisition is a very crucial step, as we do not want spurious events that are not there. In this paper, we propose an approach which avoids any requirement to repeat the surveys, by using randomized sampling technique which allows us to be more efficient in the acquisition. Our method applies to sampling data using ocean bottom nodes (OBN) as receivers. We test the efficacy of our proposed randomized acquisition geometry for time-lapse survey on two different models. In the first example, model properties does not change with time, while in the second example, model exhibit a time-lapse effect which may be caused by the migration of fluid within the reservoir. We perform two types of randomized sampling - uniform randomized sampling and jittered sampling to visualize the effects of non-repeatability in time-lapse survey. We observe that jittered randomized sampling is a more efficient method compared to randomized sampling, due to it{\textquoteright}s requirement to control the maximum spacing between the receivers. The results are presented, in the image space, as a least-squares migration of the model perturbation and they are shown for a subset of a synthetic model - the Marmousi model},
	keywords = {Acquisition, migration, time-lapse},
	url = {https://slim.gatech.edu/Publications/Public/TechReport/2013/oghenekohwo2013SEGtlswrs/oghenekohwo2013SEGtlswrs.pdf},
	author = {Felix Oghenekohwo and Felix J. Herrmann}
}
