@mastersthesis {zhang2018THlss,
	title = {Large-scale seismic data compression: application to full waveform inversion and extended image volume},
	year = {2018},
	note = {(MSc)},
	month = {04},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Conventional oil and gas fields are increasingly difficult to explore and im- age, resulting in a call for more complex wave-equation based inversion al- gorithms that require dense long-o↵set samplings. Consequently, there is an exponential growth in the size of data volumes and prohibitive demands on computational resources. In this work, we propose a method to com- press and process seismic data directly in a low-rank tensor format, which drastically reduces the amount of storage required to represent the data. We first outline how seismic data exhibits low-rank structure in a particular transform-domain, which can be exploited to compress the dense data in one extremely storage-efficient tensor format when the data is fully sampled. In the more realistic case of missing data, we can use interpolation techniques based on the same tensor format to recover fully sampled data volume in compressed form. In either case, once we have our data represented in its compressed tensor form, we design an algorithm to extract source or receiver gathers directly from the compressed parameters. This extraction process can be done on-the-fly directly on the compressed data, in the full wave- form inversion context, and does not require scanning through the entire dataset in order to form shot gathers. To the best of our knowledge, this work is one of the first major contributions to working with seismic data applications directly in the compressed domain without reconstructing the entire data volume. We use a stochastic inversion approach, which works with small subsets of source experiments at each iteration, further to reduce the computational and memory costs of full waveform inversion. We also demonstrate how this data compression and extraction technique can be ap- plied to forming full subsurface image gathers through probing techniques.},
	keywords = {compression, extended image volume, FWI, hierarchical tucker, MSc, multilinear algebra, on-the-fly, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2018/zhang2018THlss/zhang2018THlss.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2018/zhang2018THlss/zhang2018THlss_pres.pdf},
	author = {Yiming Zhang}
}
@mastersthesis {oghenekohwo2017THetl,
	title = {Economic time-lapse seismic acquisition and imaging{\textendash}-Reaping the benefits of randomized sampling with distributed compressive sensing},
	year = {2017},
	note = {(PhD)},
	month = {08},
	school = {The University of British Columbia},
	type = {phd},
	address = {Vancouver},
	abstract = {This thesis presents a novel viewpoint on the implicit opportunities randomized surveys bring to time-lapse seismic - which is a proven surveillance tool for hydrocarbon reservoir monitoring. Time-lapse (4D) seismic combines acquisition and processing of at least two seismic datasets (or vintages) in order to extract information related to changes in a reservoir within a specified time interval. The current paradigm places stringent requirements on replicating the 4D surveys, which is an expensive task often requiring uneconomical dense sampling of seismic wavefields. To mitigate the challenges of dense sampling, several advances in seismic acquisition have been made in recent years including the use of multiple sources firing at near simultaneous random times, and the adaptation of Compressive Sensing (CS) principles to design practical acquisition engines that improve sampling efficiency for seismic data acquisition. However, little is known regarding the implications of these developments for time-lapse studies. By conducting multiple experiments modelling surveys adhering to the principles of CS for 4D seismic, I propose a model that demonstrates the feasibility of randomized acquisitions for time-lapse seismic. The proposed joint recovery model (JRM), which derives from distributed CS, exploits the common information in time-lapse data during recovery of dense wavefields from measured subsampled data, providing highly repeatable and high-fidelity vintages. I show that we obtain better vintages when randomized surveys are not replicated, in contrast to standard practice, paving the way for an opportunity to relax the rigorous requirement to replicate surveys precisely. We assert that the vintages obtained using our proposed model are of sufficient quality to serve as inputs to processes that extract time-lapse attributes from which subsurface changes are deduced. Additionally, I show that recovery with the JRM is robust with respect to errors due to differences between actual and recorded postplot information. Finally, I present an opportunity to adapt our model to problems related to time-lapse seismic imaging where the main finding is that we can better delineate time-lapse changes by adapting the joint recovery model to wave-equation based inversion methods.},
	keywords = {Acquisition, Compressive Sensing, distributed compressive sensing, joint recovery, PhD, thesis, time lapse},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2017/oghenekohwo2017THetl/oghenekohwo2017THetl_pres.pdf},
	author = {Felix Oghenekohwo}
}
@mastersthesis {bougher2016THmla,
	title = {Machine learning applications to geophysical data analysis},
	year = {2016},
	note = {(MSc)},
	month = {08},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {The sedimentary layers of the Earth are a complex amorphous material formed from chaotic, turbulent, and random natural processes. Exploration geophysicists use a combination of assumptions, approximate physical models, and trained pattern recognition to extract useful information from complex remote sensing data such as seismic and well logs. In this thesis I investigate supervised and unsupervised machine learning models in geophysical data analysis and present two novel applications to exploration geophysics. Firstly, interpreted well logs from the Trenton-Black River study are used to train a classifier that results in a success rate of 67\% at predicting stratigraphic units from gamma ray logs. I use the scattering transform, a multiscale analysis transform, to extract discriminating features to feed a K-nearest neighbour classifier. A second experiment frames a conventional pre-stack seismic data characterization workflow as an unsupervised machine learning problem that is free from physical assumptions. Conventionally, the Shuey model is used to fit the angle dependent reflectivity response of seismic data. I instead use principle component based approaches to learn projections from the data that improve classification. Results on the Marmousi II elastic model and an industry field dataset show that unsupervised learning models can be effective at segmenting hydrocarbon reservoirs from seismic data.},
	keywords = {AVA, machine learning, MSc, PCA, scattering transform, thesis, well logs},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2016/bougher2016THmla/bougher2016THmla.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2016/bougher2016THmla/bougher2016THmla_pres.pdf},
	author = {Ben B. Bougher}
}
@mastersthesis {li2015THwmd,
	title = {A weighted $\ell_1$-minimization for distributed compressive sensing},
	year = {2015},
	note = {(MSc)},
	month = {09},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Distributed Compressive Sensing (DCS) studies the recovery of jointly sparse signals. Compared to separate recovery, the joint recovery algorithms in DCS are usually more effective as they make use of the joint sparsity. In this thesis, we study a weighted l1-minimization algorithm for the joint sparsity model JSM-1 proposed by Baron et al. Our analysis gives a sufficient null space property for the joint sparse recovery. Furthermore, this property can be extended to stable and robust settings. We also presents some numerical experiments for this algorithm.},
	keywords = {distributed compressive sensing, MSc, thesis, weighted $\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/li2015THwmd/li2015THwmd.pdf},
	author = {Xiaowei Li}
}
@mastersthesis {petrenko2014THaih,
	title = {Accelerating an iterative Helmholtz solver using reconfigurable hardware},
	year = {2014},
	note = {(MSc)},
	month = {04},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {An implementation of seismic wave simulation on a platform consisting of a conventional host processor and a reconfigurable hardware accelerator is presented. This research is important in the field of exploration for oil and gas resources, where a 3D model of the subsurface of the Earth is frequently required. By comparing seismic data collected in a real-world survey with synthetic data generated by simulated waves, it is possible to deduce such a model. However this requires many time-consuming simulations with different Earth models to find the one that best fits the measured data. Speeding up the wave simulations would allow more models to be tried, yielding a more accurate estimate of the subsurface. The reconfigurable hardware accelerator employed in this work is a field programmable gate array (FPGA). FPGAs are computer chips that consist of electronic building blocks that the user can configure and reconfigure to represent their algorithm in hardware. Whereas a traditional processor can be viewed as a pipeline for processing instructions, an FPGA is a pipeline for processing data. The chief advantage of the FPGA is that all the instructions in the algorithm are already hardwired onto the chip. This means that execution time depends only on the amount of data to be processed, and not on the complexity of the algorithm. The main contribution is an implementation of the well-known Kaczmarz row projection algorithm on the FPGA, using techniques of dataflow programming. This kernel is used as the preconditioning step of CGMN, a modified version of the conjugate gradients method that is used to solve the time-harmonic acoustic isotropic constant density wave equation. Using one FPGA-based accelerator, the current implementation allows seismic wave simulations to be performed over twice as fast, compared to running on one Intel Xeon E5-2670 core. I also discuss the effect of modifications of the algorithm necessitated by the hardware on the convergence properties of CGMN. Finally, a specific plan for future work is set-out in order to fully exploit the accelerator platform, and the work is set in its larger context.},
	keywords = {CG, CGMN, FPGA, Helmholtz, Kaczmarz, linear solver, Maxeler, MSc, thesis, wave equation},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/petrenko2014THaih/petrenko2014THaih.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2014/petrenko2014THaih/petrenko2014THaih_pres.pdf},
	author = {Art Petrenko}
}
@mastersthesis {miao2014THesi,
	title = {Efficient seismic imaging with spectral projector and joint sparsity},
	year = {2014},
	note = {(MSc)},
	month = {03},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {In this thesis, we investigate the potential of improving the eciency of seismic imaging with two advanced techniques: the spectral projector and the {\textquoteright}joint sparsity{\textquoteright}. The spectral projector offers an eigenvalue decomposition free computation routine that can filter out unstable evanescent wave com- ponents during wave equation based depth extrapolation. {\textquoteright}Joint sparsity{\textquoteright} aims to improve on the pure sparsity promoting recovery by making use of additional structure information of the signal. Besides, a new sparsity optimization algorithm - PQNL1 - is proposed to improve both theoretical convergence rate and practical performance for extremely large seismic imaging problems.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi.pdf},
	presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2014/miao2014THesi/miao2014THesi_pres.pdf},
	author = {Lina Miao}
}
@mastersthesis {hargreaves2014THssr,
	title = {Sparse signal recovery: analysis and synthesis formulations with prior support information},
	year = {2014},
	note = {(MSc)},
	month = {04},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {The synthesis model for signal recovery has been the model of choice for many years in compressive sensing. Various weighting schemes using prior support information to adjust the objective function associated with the synthesis model have been shown to improve the recovery of the signal in terms of accuracy. Generally, even with no prior knowledge of the support, iterative methods can build support estimates and incorporate that into the recovery which has also been shown to increase the speed and accuracy of the recovery. However when the original signal is sparse with respect to a redundant dictionary (rather than an orthonormal basis) there is a counterpart model to synthesis, namely the analysis model, which has been less popular but has recently attracted more attention. The analysis model is much less understood and thus there are fewer theorems available in both the context of non-weighted and weighted signal recovery. In this thesis, we investigate weighting in both the analysis model and synthesis model in weighted $\ell_1$-minimization. Theoretical guarantees on reconstruction and various weighting strategies for each model are discussed. We give conditions for weighted synthesis recovery with frames which do not require strict incoherency conditions, this is based on recent results of regular synthesis with frames using optimal dual $\ell_1$ analysis. A novel weighting technique is introduced in the analysis case which outperforms its traditional counterparts in the case of seismic wavefield reconstruction. We also introduce a weighted split Bregman algorithm for analysis and optimal dual analysis. We then investigate these techniques on seismic data and synthetically created test data using a variety of frames.},
	keywords = {analysis, MSc, sparse, synthesis, thesis, weighted $\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/hargreaves2014THssr/hargreaves2014THssr.pdf},
	author = {Brock Hargreaves}
}
@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}
}
@mastersthesis {ghadermarzy2013THups,
	title = {Using prior support information in compressed sensing},
	year = {2013},
	note = {(MSc)},
	month = {08},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Compressed sensing is a data acquisition technique that entails recovering estimates of sparse and compressible signals from $n$ linear measurements, significantly fewer than the signal ambient dimension $N$. In this thesis we show how we can reduce the required number of measurements even further if we incorporate prior information about the signal into the reconstruction algorithm. Specifically, we study certain weighted nonconvex $\ell_p$ minimization algorithms and a weighted approximate message passing algorithm. In Chapter 1 we describe compressed sensing as a practicable signal acquisition method in application and introduce the generic sparse approximation problem. Then we review some of the algorithms used in compressed sensing literature and briefly introduce the method we used to incorporate prior support information into these problems. In Chapter 2 we derive sufficient conditions for stable and robust recovery using weighted $\ell_p$ minimization and show that these conditions are better than those for recovery by regular $\ell_p$ and weighted $\ell_1$. We present extensive numerical experiments, both on synthetic examples and on audio, and seismic signals. In Chapter 3 we derive weighted AMP algorithm which iteratively solves the weighted $\ell_1$ minimization. We also introduce a reweighting scheme for weighted AMP algorithms which enhances the recovery performance of weighted AMP. We also apply these algorithms on synthetic experiments and on real audio signals.},
	keywords = {compressed sensing, MSc, thesis, weighted $\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2013/ghadermarzy2013THups.pdf},
	author = {Navid Ghadermarzy}
}
@mastersthesis {jumah2012THdre,
	title = {Dimensionality-reduced estimation of primaries by sparse inversion},
	year = {2012},
	note = {(MSc)},
	month = {02},
	school = {The University of British Columbia},
	type = {masters},
	address = {Vancouver},
	abstract = {Data-driven methods{\textemdash}such as the estimation of primaries by sparse inversion suffer from the curse of dimensionality that leads to disproportional growth in computational and storage demands when moving to realistic 3D field data. To remove this fundamental impediment, we propose a dimensionality-reduction technique where the data matrix is approximated adaptively by a randomized low-rank factorization. Compared to conventional methods, which need passes through all the data possibly including on-the-fly interpolations for each iteration, our approach has the advantage that the passes is reduced to one to three. In addition, the low-rank matrix factorization leads to considerable reductions in storage and computational costs of the matrix multiplies required by the sparse inversion. Application of the proposed formalism to synthetic and real data shows that significant performance improvements in speed and memory use are achievable at a low computational overhead required by the low-rank factorization.},
	keywords = {MSc, thesis},
	url = {https://slim.gatech.edu/Publications/Public/Thesis/2012/jumah2012THdre.pdf},
	author = {Bander Jumah}
}
