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masterthesis.bib
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% This file was created with JabRef 2.6.
% Encoding: MacRoman
@MASTERSTHESIS{alhashim09msc,
author = {Fadhel Abbas Alhashim},
title = {Seismic Data Processing with the Parallel Windowed Curvelet Transform
Seismic Data Processing with the Parallel Windowed Curvelet Transform},
school = {University of British Columbia},
year = {2009},
type = {masters},
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 {\textquoteright}{\textquoteright}matched{\textquoteright}{\textquoteright}
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 pro- cess
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},
presentation = {http://slim.eos.ubc.ca/Publications/public/presentations/2009/alhashim09sdp1.pdf}
}
@MASTERSTHESIS{almatar10msc,
author = {Mufeed AlMatar},
title = {Estimation of Surface-free Data by Curvelet-domain Matched Filtering
and Sparse Inversion},
school = {University of British Columbia},
year = {2010},
type = {masters},
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},
url = {http://slim.eos.ubc.ca/Publications/public/theses/2010/AlMatarThesis.pdf}
}
@MASTERSTHESIS{dupuis05msc,
author = {Catherine Dupuis},
title = {Seismic singularity characterization with redundant dictionaries},
school = {The University of British Columbia},
year = {2005},
type = {masters},
address = {Vancouver, BC Canada},
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 tran- sitions,
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 corre- sponding 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 ma jor 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 1 minimizations solved by an iterative
thresholding algorithm. We present the method and show numerical
results on both synthetic and real data.},
keywords = {SLIM},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2005/dupuis05msc.pdf}
}
@MASTERSTHESIS{hennenfent08phd,
author = {Gilles Hennenfent},
title = {Sampling and reconstruction of seismic wavefields in the curvelet
domain},
school = {The University of British Columbia},
year = {2008},
type = {phd},
address = {Vancouver, BC Canada},
month = {May},
abstract = {Wavefield reconstruction is a crucial step in the seismic processing
flow. For instance, unsuccessful interpolation leads to erroneous
multiple predictions that adversely affect the performance of multiple
elimination, and to imaging artifacts. We present a new non-parametric
transform-based reconstruction method that exploits the compression
of seismic data by the recently developed curvelet transform. The
elements of this transform, called curvelets, are multi-dimensional,
multi-scale, and multi-directional. They locally resemble wavefronts
present in the data, which leads to a compressible representation
for seismic data. This compression enables us to formulate a new
curvelet-based seismic data recovery algorithm through sparsity-promoting
inversion (CRSI). The concept of sparsity-promoting inversion is
in itself not new to geophysics. However, the recent insights from
the field of {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright}
are new since they clearly identify the three main ingredients that
go into a successful formulation of a reconstruction problem, namely
a sparsifying transform, a sub-Nyquist sampling strategy that subdues
coherent aliases in the sparsifying domain, and a data-consistent
sparsity-promoting program. After a brief overview of the curvelet
transform and our seismic-oriented extension to the fast discrete
curvelet transform, we detail the CRSI formulation and illustrate
its performance on synthetic and real datasets. Then, we introduce
a sub-Nyquist sampling scheme, termed jittered undersampling, and
show that, for the same amount of data acquired, jittered data are
best interpolated using CRSI compared to regular or random undersampled
data. We also discuss the large-scale one-norm solver involved in
CRSI. Finally, we extend CRSI formulation to other geophysical applications
and present results on multiple removal and migration-amplitude recovery.},
keywords = {curvelet transform, reconstruction, SLIM},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/hennenfent08phd.pdf}
}
@MASTERSTHESIS{Kumar09msc,
author = {Vishal Kumar},
title = {Incoherent noise suppression and deconvolution using curvelet-domain
sparsity},
school = {University of British Columbia},
year = {2009},
type = {masters},
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},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2009/kumar09ins.pdf}
}
@MASTERSTHESIS{lebed08msc,
author = {Evgeniy Lebed},
title = {Sparse Signal Recovery in a Transform Domain},
school = {The University of British Columbia},
year = {2008},
type = {masters},
month = {August},
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, shear- lets, and many other types of {\textquoteright}x-lets{\textquoteright}
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 recov- ering
seismic wavefields from incomplete measurements.},
keywords = {MSc, SLIM},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/lebed08msc.pdf}
}
@MASTERSTHESIS{lin08cco,
author = {Tim T.Y. Lin},
title = {Compressed computation of large-scale wavefield extrapolation in
inhomogeneous medium},
year = {2008},
type = {masters},
month = {April},
abstract = {In this work an explicit algorithm for the extrapolation of one-way
wavefields is proposed which combines recent developments in information
theory and theoretical signal processing with the physics of wave
propagation. Because of excessive memory requirements, explicit formulations
for wave propagation have proven to be a challenge in 3-D. By using
ideas from {\textquoteleft}{\textquoteleft}compressed sensing{\textquoteright}{\textquoteright},
we are able to formulate the (inverse) wavefield extrapolation problem
on small subsets of the data volume, thereby reducing the size of
the operators. Compressed sensing entails a new paradigm for signal
recovery that provides conditions under which signals can be recovered
from incomplete samplings by \emph{nonlinear} recovery methods that
promote sparsity of the to-be-recovered signal. According to this
theory, signals can successfully be recovered when the measurement
basis is \emph{incoherent} with the representation in which the wavefield
is sparse. In this new approach, the eigenfunctions of the Helmholtz
operator are recognized as a basis that is incoherent with sparsity
transforms that are known to compress seismic wavefields. By casting
the wavefield extrapolation problem in this framework, wavefields
can successfully be extrapolated in the modal domain, despite evanescent
wave modes. The degree to which the wavefield can be recovered depends
on the number of missing (evanescent) wave modes and on the complexity
of the wavefield. A proof of principle for the {\textquoteleft}{\textquoteleft}compressed
sensing{\textquoteright}{\textquoteright} method is given for inverse
wavefield extrapolation in 2-D. The results show that our method
is stable, has reduced dip limitations and handles evanescent waves
in inverse extrapolation.},
keywords = {BSc, SLIM},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/timlin08bsch.pdf}
}
@MASTERSTHESIS{maysami08msc,
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},
type = {masters},
address = {Vancouver, BC Canada},
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 = {SLIM},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/maysami08msc.pdf}
}
@MASTERSTHESIS{moghaddam10phd,
author = {Peyman P. Moghaddam},
title = {Curvelet-Based Migration Amplitude Recovery},
school = {The University of British Columbia},
year = {2010},
type = {phd},
address = {Vancouver, BC Canada},
month = {May},
abstract = {Migration can accurately locate reflectors in the earth but in most
cases fails to correctly resolve their amplitude. This might lead
to mis-interpretation of the nature of reflector. In this thesis,
I introduced a method to accurately recover the amplitude of the
seismic reflector. This method relies on a new transform-based recovery
that exploits the expression of seismic images by the recently developed
curvelet transform. The elements of this transform, called curvelets,
are multi-dimensional, multi-scale, and multi-directional. They also
remain approximately invariant under the imaging operator. I exploit
these properties of the curvelets to introduce a method called Curvelet
Match Filtering (CMF) for recovering the seismic amplitude in presence
of noise in both migrated image and data. I detail the method and
illustrate its performance on synthetic dataset. I also extend CMF
formulation to other geophysical applications and present results
on multiple removal. In addition of that, I investigate preconditioning
of the migration which results to rapid convergence rate of the iterative
method using migration.},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2010/moghaddam10phd.pdf}
}
@MASTERSTHESIS{cyarham08msc,
author = {Carson Yarham},
title = {Seismic ground-roll separation using sparsity promoting L1 minimization},
school = {The University of British Columbia},
year = {2008},
type = {masters},
address = {Vancouver, BC Canada},
month = {May},
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 major 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.},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/cyarham08msc.pdf}
}
@MASTERSTHESIS{yarham08msc,
author = {Carson Yarham},
title = {Seismic ground-roll separation using sparsity promoting l1 minimization},
school = {The University of British Columbia},
year = {2008},
address = {Vancouver, BC Canada},
month = {May},
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 informa- tion 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 pres- ence 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. },
bdsk-url-1 = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/cyarham08msc.pdf},
date-added = {2008-05-20 13:43:05 -0700},
date-modified = {2008-08-14 14:33:19 -0700},
keywords = {signal separation, curvelet transform, SLIM},
pdf = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/cyarham08msc.pdf},
url = {http://slim.eos.ubc.ca/Publications/Public/Theses/2008/cyarham08msc.pdf}
}