Call number: LD2668 .T4 1979 F73 / Master of Arts

The integration of seismic attributes and well data is an important step in the development of reservoir models. These models draw upon large data sets including information from well logs, production history, seismic interpretation, and depositional models. Modern integration techniques use the extensive data sets to develop precise models using complex workflows at increased cost of time and computational power. However, a gap exists in which a geostatistically driven procedure could integrate pattern statistics inferred from seismic images and those integrated from analogous geologic systems in order to develop spatially accurate reservoir models. Direct Sampling Seismic Integration Process, DSSIP, was first proposed by Henke and Srinivasan (2010) as an alternative to traditional seismic integration methods. The process provides a probabilistic mapping tool for fast reservoir analysis based on sparse conditioning data in a target reservoir and fully interpreted data from an analog field. DSSIP combines the structural information present in seismic data and facies patterns present in a training reservoir to create a fully realized output map for the target field. In this work, the basic DSSIP algorithm has been further optimized by performing a detailed parameter sensitivity study. The basic DSSIP algorithm has been demonstrated for a real field data set for a deepwater Gulf of Mexico reservoir. The basic DSSIP algorithm has also been analyzed to understand and model the effects of features such as salt canopy that can blur the seismic image. Finally, a modification to the basic algorithm is also presented that uses only a training model and the seismic data for the target reservoir in order to generate reservoir models for the target reservoir. This procedure eliminates the requirement to have a matching pair of training data sets for both the facies distribution and the corresponding seismic response. / text

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Foreground extraction technology plays an important role in image and video processing tasks. It has been widely used in various industries. To better describe the overlap relationship between foreground and background, alpha channel is introduced. It reveals the opacity property of foreground objects. Thus, fully extracting a foreground object requires determining the alpha values for pixels, also known as extracting an alpha matte. In this thesis, we propose an improved sampling-based alpha matting algorithm, which is capable of generating high quality matting results. By analyzing the weakness of previous approaches, we optimize the sampling process and consider the cost of each sample pair to avoid missing any good samples. The good performance is demonstrated even for complex images. On the other hand, extracting foreground objects from video sequences is a more challenging task since it has higher demands on accuracy and efficiency. Previous approaches usually require a significant amount of user input and the results still suffer from inaccuracy. In this thesis, we successfully extend our algorithm to video sequences and let it run in an automatic fashion. Adaptive trimap, which is vital for matting, can be automatically generated and properly propagated in this system. Our method not only reduces the user interference but also guarantees the matting quality.

A simple modification to a standard increment borer handle is described that enhances use of the borer in situations where obstructions to the rotation of a normal handle prevents utilization of the full length of the borer. The modification, informally called the ‘‘Quad-B’’ (Brown’s bent boomerang borer handle), involves bending both sides of the handle to ~35–40° angles. Some potential uses of the modified handle are described.

Spline smoothing is a popular technique for creating maps of a spatial phenomenon. Most smoothers use the Euclidean metric to measure the distance between data. This approach is flawed since the distances between points in the domain as experienced by the objects within the domain are rarely Euclidean. For example, the movements of animals and people are subject to both physical and political boundaries (respectively) which must be navigated. Measuring distances between the objects using the incorrect (Euclidean) metric leads to incorrect inference. The first part of this thesis develops a finite area smoother which does not su↵er from this problem when the shape of the area is complex. It begins by rejecting the use of the Schwarz-Christo↵el transform as a method for morphing complex domains due to its squashing of space. From there a method based on preserving within-area distances using multidimensional scaling is developed. High dimensional projections of the data are necessary to avoid a loss of ordering in the points. To smooth reliably in high dimensions Duchon splines are used. The model developed rivals the current best finite area method in prediction error terms and fits easily into larger models. Finally, the utility of projection methods to smooth general distances is explored. The second part of the thesis concerns distance sampling, a widely used set of methods for estimating the abundance of biological populations. The work presented here introduces mixture formulation for the detection function used to model the probability of detection. The use of mixture models leads to flexible but monotonic detection functions, avoiding the unrealistic shapes which conventional methods are prone to. These new models are then applied to several existing, problematic data sets.

Chan For Yee. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2005. / Includes bibliographical references (leaves 88-90). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Basic Concepts --- p.6 / Chapter 2.1 --- Binomial vs Inverse Sampling --- p.6 / Chapter 2.2 --- Equivalence / Non-inferiority Test --- p.7 / Chapter 3 --- Testing Procedures --- p.9 / Chapter 3.1 --- The Model --- p.9 / Chapter 3.2 --- Asymptotic Behaviors of the Estimators --- p.10 / Chapter 3.2.1 --- Asymptotic Test Statistic based on Unconditional Maximum Likelihood Estimate --- p.12 / Chapter 3.2.2 --- Asymptotic Test Statistic based on restricted maximum likelihood estimate --- p.13 / Chapter 3.3 --- Conditional Exact Procedures --- p.16 / Chapter 3.3.1 --- Non-test-statistic-based procedure --- p.17 / Chapter 3.3.2 --- Test-statistic-based procedure --- p.17 / Chapter 4 --- Simulation Study --- p.19 / Chapter 4.1 --- Simulation Results - Type I error rate --- p.21 / Chapter 4.1.1 --- Asymptotic Test Statistic based on Unconditional MLE . . --- p.21 / Chapter 4.1.2 --- Asymptotic Test Statistic based on Restricted MLE . . . . --- p.22 / Chapter 4.1.3 --- Non-test-statistic-based Conditional Exact Test --- p.23 / Chapter 4.1.4 --- Test-statistic-based Conditional Exact Test --- p.24 / Chapter 4.2 --- Simulation Results - Power --- p.25 / Chapter 4.2.1 --- Asymptotic Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.25 / Chapter 4.2.2 --- Conditional Exact Tests - Similarity and Difference be- tween using Non-test-statistic-based and Test-statistic-based Procedures --- p.30 / Chapter 4.2.3 --- Test-statistic-based Conditional Exact Tests - Similarity and Difference between using Unconditional and Restricted MLE --- p.31 / Chapter 5 --- Conclusion --- p.32 / Appendices --- p.36 / Chapter A. --- Simulation Result - Type I error rate --- p.36 / Chapter B. --- Simulation Result - Power value --- p.42 / Bibliography --- p.88