@mastersthesis {johnson2013THswr,
	title = {Seismic wavefield reconstruction using reciprocity},
	year = {2013},
	note = {(MSc)},
	month = {03},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {The primary focus of most reflection seismic surveys is to help locate hydrocarbon recourses. Due to an ever increasing scarcity of these recourses, we must increase the size and quality of our seismic surveys. However, processing such large seismic data volumes to accurately recover earth properties is a painstaking and computationally intensive process. Due to the way reflection seismic surveys are conducted there are often holes in the collected data, where traces are not recorded. This can be due to physical or cost constraints. For some of the initial stages of processing these missing traces are of little consequence. However processes like multiple prediction and removal, interferometric ground roll prediction, and migration require densely sampled data on a regular grid. Thus the need to interpolate undersampled data cannot be ignored. Using the fact that reflection seismic data sets obey a reciprocal relationship in source and receiver locations, combined with recent advances in the field of compressed sensing, we show that properly regularized the wavefield reconstruction problem can be solved with a high degree of accuracy. We exploit the compressible nature of seismic data in the curvelet domain to solve regularized l1 recovery problems that seek to match the measured data and enforce the above mentioned reciprocity. Using our method we were able to achieve results with a 20.45 dB signal to noise ratio when reconstructing a marine data set that had 50\% of its traces decimated. This is a 13.44 dB improvement over using the same method run without taking reciprocity into account.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2013/johnson2013THswr.pdf},
	author = {James Johnson}
}
@conference {johnson2010EAGEeop,
	title = {Estimation of primaries via sparse inversion with reciprocity},
	booktitle = {EAGE Annual Conference Proceedings},
	year = {2010},
	abstract = {Accurate removal of surface related multiples is a key step in seismic data processing. The industry standard for removing multiples is SRME, which involves convolving the data with itself to predict the multiples, followed by an adaptive subtraction procedure to recover the primaries (Verschuur and Berkhout, 1997). Other methods involve multidimensional division of the up-going and down-going wavefields (Amundsen, 2001). However, this approach may suffer from stability problems. With the introduction of the {\textquoteleft}{\textquoteleft}estimation of primaries by sparse inversion{\textquoteright}{\textquoteright}(EPSI), van Groenestijn and Verschuur (2009) recentely reformulated SRME to jointly estimate the surface-free impulse response and the source signature directly from the data. The advantage of EPSI is that it recovers the primary response directly, and does not require a second processing step for the subtraction of estimated multiples from the original data. However, because it estimates both the primary impulse response and source signature from the data EPSI must be regularized. Motivated by recent successful application of the curvelet transform in seismic data processing (Herrmann et al., 2007), we formulate EPSI as a bi-convex optimization problem that seeks sparsity on the surface-free Green{\textquoteright}s function and Fourier-domain smoothness on the source wavelet. Our main contribution compared to previous work (Lin and Herrmann, 2009), and the contribution of that author to the proceedings of this meeting(Lin and Herrmann, 2010), is that we employ the physical principle of as source-receiver reciprocity to improve the inversion.},
	keywords = {EAGE},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/EAGE/2010/johnson10EAGEeop/johnson10EAGEeop.pdf},
	author = {James Johnson and Tim T.Y. Lin and Felix J. Herrmann}
}
@conference {johnson2008SINBADsdi,
	title = {Seismic data interpolation with symmetry},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Due to the physics of reciprocity seismic data sets are symmetric in the source and receiver coordinates. Often seismic data sets are incomplete and the missing data must be interpolated. Typically, missing traces do not occur symmetrically. The purpose of this project is to extend the current formulation for solving the seismic interpolation problems in such a way that they enforce reciprocity. The method decomposes the seismic data volume into symmetric and antisymmetric parts. This decomposition leads to an augmented system of equations for the L1-solver that promotes sparsity in the curvelet domain. Interpolation is carried out on the entire system during which the asymmetric component of the volume is forced to zero, while the symmetric part of the data volume is matched to the measured data.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/johnson2008SINBADsdi/johnson2008SINBADsdi.pdf},
	author = {James Johnson and Gilles Hennenfent}
}
