masterthesis.bib

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@MASTERSTHESIS{zhang2018THlss,
  author = {Yiming Zhang},
  title = {Large-scale seismic data compression: application to full waveform inversion and extended image volume},
  school = {The University of British Columbia},
  year = {2018},
  month = {04},
  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 = {MSc, thesis, compression, FWI, extended image volume, on-the-fly, multilinear algebra, Hierarchical Tucker},
  note = {(MSc)},
  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}
}

@MASTERSTHESIS{bougher2016THmla,
  author = {Ben B. Bougher},
  title = {Machine learning applications to geophysical data analysis},
  school = {The University of British Columbia},
  year = {2016},
  month = {08},
  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 = {MSc, thesis, machine learning, PCA, AVA, well logs, scattering transform},
  note = {(MSc)},
  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}
}


@MASTERSTHESIS{li2015THwmd,
  author = {Xiaowei Li},
  title = {A weighted $\ell_1$-minimization for distributed compressive sensing},
  school = {The University of British Columbia},
  year = {2015},
  month = {09},
  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 = {MSc, thesis, weighted $\ell_1$, distributed compressive sensing},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2015/li2015THwmd/li2015THwmd.pdf}
}


@MASTERSTHESIS{hargreaves2014THssr,
  author = {Brock Hargreaves},
  title = {Sparse signal recovery: analysis and synthesis formulations with prior support information},
  school = {The University of British Columbia},
  year = {2014},
  month = {04},
  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 = {MSc, thesis, sparse, analysis, synthesis, weighted $\ell_1$},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2014/hargreaves2014THssr/hargreaves2014THssr.pdf}
}


@MASTERSTHESIS{petrenko2014THaih,
  author = {Art Petrenko},
  title = {Accelerating an iterative {Helmholtz} solver using reconfigurable hardware},
  school = {The University of British Columbia},
  year = {2014},
  month = {04},
  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},
  note = {(MSc)},
  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}
}


@MASTERSTHESIS{miao2014THesi,
  author = {Lina Miao},
  title = {Efficient seismic imaging with spectral projector and joint sparsity},
  school = {The University of British Columbia},
  year = {2014},
  month = {03},
  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
                  'joint sparsity'. 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. 'Joint sparsity' 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},
  note = {(MSc)},
  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}
}


@MASTERSTHESIS{ghadermarzy2013THups,
  author = {Navid Ghadermarzy},
  title = {Using prior support information in compressed sensing},
  school = {The University of British Columbia},
  year = {2013},
  month = {08},
  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 = {MSc, thesis, compressed sensing, weighted $\ell_1$},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2013/ghadermarzy2013THups.pdf}
}


@MASTERSTHESIS{johnson2013THswr,
  author = {James Johnson},
  title = {Seismic wavefield reconstruction using reciprocity},
  school = {The University of British Columbia},
  year = {2013},
  month = {03},
  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},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2013/johnson2013THswr.pdf}
}


@MASTERSTHESIS{alhashim09THsdp,
  author = {Fadhel Abbas Alhashim},
  title = {Seismic data processing with the parallel windowed curvelet transform},
  school = {The University of British Columbia},
  year = {2009},
  month = {08},
  address = {Vancouver},
  abstract = {The process of obtaining high quality seismic images is
                  very challenging when exploring new areas that have
                  high complexities. The to be processed seismic data
                  comes from the field noisy and commonly incomplete.
                  Recently, major advances were accomplished in the
                  area of coherent noise removal, for example, Surface
                  Related Multiple Elimination (SRME). Predictive
                  multiple elimination methods, such as SRME, consist
                  of two steps: The first step is the prediction step,
                  in this step multiples are predicted from the
                  seismic data. The second step is the separation step
                  in which primary reflection and surface related
                  multiples are separated, this involves predicted
                  multiples from the first step to be matched with the
                  true multiples in the data and eventually removed. A
                  recent robust Bayesian wavefield separation method
                  have been recently introduced to improve on the
                  separation by matching methods. This method utilizes
                  the effectiveness of using the multi scale and multi
                  angular curvelet transform in processing seismic
                  images. The method produced excellent results and
                  improved multiple removal. A considerable problem in
                  the seismic processing field is the fact that
                  seismic data are large and require a correspondingly
                  large memory size and processing time. The fact that
                  curvelets are redundant also increases the need for
                  large memory to process seismic data. In this thesis
                  we propose a parallel approach based windowing
                  operator that divides large seismic data into
                  smaller more managable datasets that can fit in
                  memory so that it is possible to apply the Bayesian
                  separation process in parallel with minimal harm to
                  the image quality and data integrity. However, by
                  dividing the data, we introduce discontinuities. We
                  take these discontinuities into account and compare
                  two ways that different windows may communicate.
                  The first method is to communicate edge information
                  at only two steps, namely, data scattering and
                  gathering processes while applying the multiple
                  separation on each window separately. The second
                  method is to define our windowing operator as a
                  global operator, which exchanges window edge
                  information at each forward and inverse curvelet
                  transform.  We discuss the trade off between the two
                  methods trying to minimize complexity and I/O time
                  spent in the process. We test our windowing operator
                  on a seismic denoising problem and then apply the
                  windowing operator on our sparse-domain Bayesian
                  primary-multiple separation.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2009/alhashim09THsdp.pdf},
  presentation = {https://slim.gatech.edu/Publications/Public/Thesis/2009/alhashim09THsdp_pres.pdf}
}


@MASTERSTHESIS{jumah2012THdre,
  author = {Bander Jumah},
  title = {Dimensionality-reduced estimation of primaries by sparse inversion},
  school = {The University of British Columbia},
  year = {2012},
  month = {02},
  address = {Vancouver},
  abstract = {Data-driven methods—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},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2012/jumah2012THdre.pdf}
}


@MASTERSTHESIS{almatar10THesd,
  author = {Mufeed H. AlMatar},
  title = {Estimation of surface-free data by curvelet-domain matched filtering and sparse inversion},
  school = {The University of British Columbia},
  year = {2010},
  month = {12},
  address = {Vancouver},
  abstract = {A recent robust multiple-elimination technique, based on
                  the underlying principle that relates primary
                  impulse response to total upgoing wavefield, tries
                  to change the paradigm that sees surface-related
                  multiples as noise that needs to be removed from the
                  data prior to imaging. This technique, estimation of
                  primaries by sparse inversion (EPSI), (van
                  Groenestijn and Verschuur, 2009; Lin and Herrmann,
                  2009), proposes an inversion procedure during which
                  the source function and surface- free impulse
                  response are directly calculated from the upgoing
                  wavefield using an alternating optimization
                  procedure. EPSI hinges on a delicate interplay
                  between surface-related multiples and pri-
                  maries. Finite aperture and other imperfections may
                  violate this relationship. In this thesis, we
                  investigate how to make EPSI more robust by
                  incorporating curvelet-domain matching in its
                  formulation. Compared to surface-related multiple
                  removal (SRME), where curvelet-domain matching was
                  used successfully, incorporating this step has the
                  additional advantage that matches multiples to
                  multiples rather than predicated multiples to total
                  data as in SRME.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2010/almatar10THesd.pdf}
}


@MASTERSTHESIS{dupuis05THssc,
  author = {Catherine Dupuis},
  title = {Seismic singularity characterization with redundant dictionaries},
  school = {The University of British Columbia},
  year = {2005},
  month = {07},
  address = {Vancouver},
  abstract = {We consider seismic signals as a superposition of
                  waveforms parameterized by their fractional-
                  orders. Each waveform models the reflection of a
                  seismic wave at a particular transition between two
                  lithological layers in the subsurface. The location
                  of the waveforms in the seismic signal corresponds
                  to the depth of the transitions in the subsurface,
                  whereas their fractional-order constitutes a measure
                  of the sharpness of the transitions. By considering
                  fractional-order transitions, we generalize the
                  zero-order transition model of the conventional
                  deconvolution problem, and aim at capturing the
                  different types of transitions. The goal is to
                  delineate and characterize transitions from seismic
                  signals by recovering the locations and
                  fractional-orders of its corresponding
                  waveforms. This problem has received increasing
                  interest, and several methods have been proposed,
                  including multi- and monoscale analysis based on
                  Mallat{\textquoteright}s wavelet transform modulus
                  maxima, and seismic atomic decomposition. We propose
                  a new method based on a two-step approach, which
                  divides the initial problem of delineating and
                  characterizing transitions over the whole seismic
                  signal, into two easier sub-problems. The algorithm
                  first partitions the seismic signal into its major
                  components, and then estimates the fractional-orders
                  and locations of each component. Both steps are
                  based on the sparse decomposition of seismic signals
                  in overcomplete dictionaries of waveforms parameter-
                  ized by their fractional-orders, and involve
                  $\ell_1$ minimizations solved by an iterative
                  thresholding algorithm. We present the method and
                  show numerical results on both synthetic and real
                  data.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2005/dupuis05THssc.pdf}
}


@MASTERSTHESIS{kumar09THins,
  author = {Vishal Kumar},
  title = {Incoherent noise suppression and deconvolution using curvelet-domain sparsity},
  school = {The University of British Columbia},
  year = {2009},
  month = {06},
  address = {Vancouver},
  abstract = {Curvelets are a recently introduced transform domain
                  that belongs to a family of multiscale and also
                  multidirectional data expansions. As such, curvelets
                  can be applied to resolution of the issues of
                  complicated seismic wavefronts. We make use of this
                  multiscale, multidirectional and hence sparsifying
                  ability of the curvelet transform to suppress
                  incoherent noise from crustal data where the
                  signal-to-noise ratio is low and to develop an
                  improved deconvolution procedure. Incoherent noise
                  present in seismic reflection data corrupts the
                  quality of the signal and can often lead to
                  misinterpretation. The curvelet domain lends itself
                  particularly well for denoising because coherent
                  seismic energy maps to a relatively small number of
                  significant curvelet coefficents.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2009/kumar09THins.pdf}
}


@MASTERSTHESIS{lebed08THssr,
  author = {Evgeniy Lebed},
  title = {Sparse signal recovery in a transform domain},
  school = {The University of British Columbia},
  year = {2008},
  month = {08},
  address = {Vancouver},
  abstract = {The ability to efficiently and sparsely represent
                  seismic data is becoming an increasingly important
                  problem in geophysics. Over the last thirty years
                  many transforms such as wavelets, curvelets,
                  contourlets, surfacelets, shearlets, and many other
                  types of x-lets have been developed. Such transform
                  were leveraged to resolve this issue of sparse
                  representations. In this work we compare the
                  properties of four of these commonly used
                  transforms, namely the shift-invariant wavelets,
                  complex wavelets, curvelets and surfacelets. We also
                  explore the performance of these transforms for the
                  problem of recovering seismic wavefields from
                  incomplete measurements.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/lebed08THssr.pdf}
}


@MASTERSTHESIS{maysami08THlcs,
  author = {Mohammad Maysami},
  title = {Lithology constraints from seismic waveforms: application to opal-{A} to opal-{CT} transition},
  school = {The University of British Columbia},
  year = {2008},
  month = {02},
  address = {Vancouver},
  abstract = {In this work, we present a new method for seismic
                  waveform characterization, which is aimed at
                  extracting detailed litho-stratigraphical
                  information from seismic data. We attempt to
                  estimate the lithological attributes from seismic
                  data according to our parametric representation of
                  stratigraphical horizons, where the parameter values
                  provide us with a direct link to nature of
                  lithological transitions. We test our method on a
                  seismic dataset with a strong diagenetic transition
                  (opal-A to opal-CT transition).  Given some
                  information from cutting samples of well, we use a
                  percolation-based model to construct the elastic
                  profile of lithological transitions.  Our goal is to
                  match parametric representation for the diagenetic
                  transition in both real data and synthetic data
                  given by these elastic profiles. This match may be
                  interpreted as a well-seismic tie, which reveals
                  lithological information about stratigraphical
                  horizons.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/maysami08THlcs.pdf}
}


@MASTERSTHESIS{yarham08THsgs,
  author = {Carson Yarham},
  title = {Seismic ground-roll separation using sparsity promoting $\ell_1$ minimization},
  school = {The University of British Columbia},
  year = {2008},
  month = {05},
  address = {Vancouver},
  abstract = {The removal of coherent noise generated by surface waves
                  in land based seismic is a prerequisite to imaging
                  the subsurface. These surface waves, termed as
                  ground roll, overlay important reflector information
                  in both the t-x and f-k domains. Standard techniques
                  of ground roll removal commonly alter reflector
                  information as a consequence of the ground roll
                  removal. We propose the combined use of the curvelet
                  domain as a sparsifying basis in which to perform
                  signal separation techniques that can preserve
                  reflector information while increasing ground roll
                  removal. We examine two signal separation
                  techniques, a block-coordinate relaxation method and
                  a Bayesian separation method. The derivations and
                  background for both methods are presented and the
                  parameter sensitivity is examined. Both methods are
                  shown to be effective in certain situations
                  regarding synthetic data and erroneous surface wave
                  predictions. The block-coordinate relaxation method
                  is shown to have ma jor weaknesses when dealing with
                  seismic signal separation in the presence of noise
                  and with the production of artifacts and reflector
                  degradation. The Bayesian separation method is shown
                  to improve overall separation for both seismic and
                  real data. The Bayesian separation scheme is used on
                  a real data set with a surface wave prediction
                  containing reflector information. It is shown to
                  improve the signal separation by recovering
                  reflector information while improving the surface
                  wave removal. The abstract contains a separate real
                  data example where both the block-coordinate
                  relaxation method and the Bayesian separation method
                  are compared.},
  keywords = {MSc},
  note = {(MSc)},
  url = {https://slim.gatech.edu/Publications/Public/Thesis/2008/yarham08THsgs.pdf}
}

@MASTERSTHESIS{fenelon08msc,
  author = {Lloyd Fenelon},
  title = {Nonequispaced discrete curvelet transform for seismic data reconstruction},
  howpublished = {BSc thesis, Ecole Nationale Superieure De Physique de Strasbourg},
  month = {08},
  year = {2008},
  abstract = {Physical constraints during seismic acquisitions lead to
                  incomplete seismic datasets. Curvelet Reconstruction
                  with Sparsity promoting Inversion (CRSI) is one of
                  the most efficient interpolation method available to
                  recover complete datasets from data with missing
                  traces. The method uses in its definition the
                  curvelet transform which is well suited to process
                  seismic data. However, its main shortcoming is to
                  not be able to provide an accurate result if the
                  data are acquired at irregular positions. This come
                  from the curvelet transform implementation which
                  cannot handle this type of data. In this thesis the
                  implementation of the curvelet transform is modified
                  to offer the possibility to CRSI to give better
                  representation of seismic data for high quality
                  seismic imaging.},
  bdsk-url-1 = {http://slim.gatech.edu/Publications/Public/Theses/2008/fenelon08msc.pdf},
  date-added = {2008-09-03 16:18:08 -0700},
  date-modified = {2008-09-03 16:25:10 -0700},
  keywords = {SLIM, BSc},
  pdf = {http://slim.gatech.edu/Publications/Public/Theses/2008/fenelon08msc.pdf}
}