990 research outputs found
Seismic Ray Impedance Inversion
This thesis investigates a prestack seismic inversion scheme implemented in the ray
parameter domain. Conventionally, most prestack seismic inversion methods are
performed in the incidence angle domain. However, inversion using the concept of
ray impedance, as it honours ray path variation following the elastic parameter
variation according to Snell’s law, shows the capacity to discriminate different
lithologies if compared to conventional elastic impedance inversion.
The procedure starts with data transformation into the ray-parameter domain and then
implements the ray impedance inversion along constant ray-parameter profiles. With
different constant-ray-parameter profiles, mixed-phase wavelets are initially estimated
based on the high-order statistics of the data and further refined after a proper well-to-seismic
tie. With the estimated wavelets ready, a Cauchy inversion method is used to
invert for seismic reflectivity sequences, aiming at recovering seismic reflectivity
sequences for blocky impedance inversion. The impedance inversion from reflectivity
sequences adopts a standard generalised linear inversion scheme, whose results are
utilised to identify rock properties and facilitate quantitative interpretation. It has also
been demonstrated that we can further invert elastic parameters from ray impedance
values, without eliminating an extra density term or introducing a Gardner’s relation
to absorb this term.
Ray impedance inversion is extended to P-S converted waves by introducing the
definition of converted-wave ray impedance. This quantity shows some advantages in
connecting prestack converted wave data with well logs, if compared with the shearwave
elastic impedance derived from the Aki and Richards approximation to the
Zoeppritz equations. An analysis of P-P and P-S wave data under the framework of
ray impedance is conducted through a real multicomponent dataset, which can reduce
the uncertainty in lithology identification.Inversion is the key method in generating those examples throughout the entire thesis
as we believe it can render robust solutions to geophysical problems. Apart from the
reflectivity sequence, ray impedance and elastic parameter inversion mentioned above,
inversion methods are also adopted in transforming the prestack data from the offset
domain to the ray-parameter domain, mixed-phase wavelet estimation, as well as the
registration of P-P and P-S waves for the joint analysis.
The ray impedance inversion methods are successfully applied to different types of
datasets. In each individual step to achieving the ray impedance inversion, advantages,
disadvantages as well as limitations of the algorithms adopted are detailed. As a
conclusion, the ray impedance related analyses demonstrated in this thesis are highly
competent compared with the classical elastic impedance methods and the author
would like to recommend it for a wider application
Evaluating E-Relationship Marketing Features on Hotel Mobile Apps
The advent of technology has changed the course of marketing in both the academic and the business field. Given the increasing number of mobile transactions, hotel companies have launched mobile applications (apps) as an alternative e-relationship marketing (e-RM) channel. This study modified a progressive five-level e-relationship building model. The model was employed to evaluate e-RM features of the top 10 hotel companies’ mobile apps. The results indicated that these hotel companies maintained e-RM feature sophistication at the lower levels (Basic and Reactive), but relatively speaking, they did not utilize e-RM features extensively at the higher levels (Accountable, Proactive and Partnership). The findings implied that hotel companies employed mobile apps as a communication channel to provide basic information and allow for transaction rather than to deliver better customer values and strengthen long-term relationships
Extended Quark Potential Model from Random Phase Approximation
The quark potential model is extended to include the sea quark excitation
using the random phase approximation (RPA). The effective quark interaction
preserves the important Quantum Chromodynamics (QCD) properties -- chiral
symmetry and confinement simultaneously. A primary qualitive analysis shows
that the meson as a well-known typical Goldstone boson and the other
mesons made up of valence quark pair such as the meson can
also be described in this extended quark potential model
Hydrogenation and Hydro-Carbonation and Etching of Single-Walled Carbon Nanotubes
We present a systematic experimental investigation of the reactions between
hydrogen plasma and single-walled carbon nanotubes (SWNTs) at various
temperatures. Microscopy, infrared (IR) and Raman spectroscopy and electrical
transport measurements are carried out to investigate the properties of SWNTs
after hydrogenation. Structural deformations, drastically reduced electrical
conductance and increased semiconducting nature of SWNTs upon sidewall
hydrogenation are observed. These changes are reversible upon thermal annealing
at 500C via dehydrogenation. Harsh plasma or high temperature reactions lead to
etching of nanotube likely via hydro-carbonation. Smaller SWNTs are markedly
less stable against hydro-carbonation than larger tubes. The results are
fundamental and may have implications to basic and practical applications
including hydrogen storage, sensing, band-gap engineering for novel electronics
and new methods of manipulation, functionalization and etching of nanotubes.Comment: 3 pages, 4 figure
Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization
Pedestrian attribute recognition has been an emerging research topic in the
area of video surveillance. To predict the existence of a particular attribute,
it is demanded to localize the regions related to the attribute. However, in
this task, the region annotations are not available. How to carve out these
attribute-related regions remains challenging. Existing methods applied
attribute-agnostic visual attention or heuristic body-part localization
mechanisms to enhance the local feature representations, while neglecting to
employ attributes to define local feature areas. We propose a flexible
Attribute Localization Module (ALM) to adaptively discover the most
discriminative regions and learns the regional features for each attribute at
multiple levels. Moreover, a feature pyramid architecture is also introduced to
enhance the attribute-specific localization at low-levels with high-level
semantic guidance. The proposed framework does not require additional region
annotations and can be trained end-to-end with multi-level deep supervision.
Extensive experiments show that the proposed method achieves state-of-the-art
results on three pedestrian attribute datasets, including PETA, RAP, and
PA-100K.Comment: Accepted by ICCV 201
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