Prediction of groundwater inflow into rock tunnels is one of the essential tasks of tunnel engineering. Currently, most of the methods used in the industry are typically based on continuum models, whether analytical, semi-empirical, or numerical. As a consequence, a regular flow along the tunnel is commonly predicted. There are also some discrete fracture network methods based on a discontinous model, which typically yield regular flow or random flow along the tunnel. However, it was observed that, in hard rock tunnels, flow usually concentrates in some areas, and much of the tunnel is dry. The reason is that, in hard rock, most of the water flows in rock fractures and fractures typically occur in a clustered pattern rather than in a regular or random pattern. A new method is developed in this work, which can model the fracture clustering and reproduce the flow concentration. After elaborate literature review, a new algorithm is developed to simulate fractures with clustering properties by using geostatistics. Then, a discrete fracture network is built and simplified. In order to solve the flow problem in the discrete fracture network, an existing analytical-numercial method is improved. Two case studies illustrate the procedure of fracture simulation. Several ideal tunnel cases and one real tunnel project are used to validate the flow analysis. It is found that fracture clustering can be modeled and flow concentration can be reproduced by using the proposed technique. / text

Two wild blueberry fields were selected in central Nova Scotia, to characterize and quantify the spatial pattern of variability in soil properties, leaf nutrients and fruit yield, identification of yield influencing soil properties, and to develop management zones for site-specific fertilization. A combination of classical statistics, geostatistical analysis and mapping in Arc GIS 9.3 indicated substantial variation within field. The stepwise regression suggested that the soil EC, horizontal co-planar geometry (HCP), inorganic nitrogen and moisture content were major yield influencing factors. The cluster analysis of the soil variables with the fruit yield also indicated that HCP, inorganic nitrogen, EC, SOM, and ?v were closely grouped with the fruit yield at a similarity level greater than 70%. Based on the results of this study the wild blueberry fields can be divided into different management zones for variable rate fertilization to improve crop production, increase revenue, and reduce potential environmental contamination.

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No description available.

Geotechnical engineering design has relied upon deterministic methods of analysis whereby values for analysis parameters and conditions are selected subjectively based on judgment with the intent of providing acceptable margins of safety. The objective of this research was to improve the use of probabilistic slope stability analysis in practice so that the design of slopes can be made on a consistent and probabilistic basis. The current research involved the development of a methodology for the measurement and modeling of the anisotropic autocorrelation distance of cohesive soils, which was demonstrated at Dyke 17 West of the McArthur Falls Generating Station. In-situ testing using the piezocone and laboratory testing was conducted to characterize the spatial variability of the effective-shear strength envelope. Vertical (down-hole) and horizontal (cross-hole) geostatistical analysis was conducted to assess the anisotropy of the semivariogram. The investigation identified that heterogeneous inclusions had significant impacts on the results, but that simplistic (visual) identification and filtering procedures were adequate. The effective-stress shear strength envelope was statistically characterized as a random field, which was simulated as a first-order Markov process using customized add-in functions in a limit-equilibrium slope stability analysis. The analysis accounts for the spatial variability of shear strength and is capable of simulating both isotropic and anisotropic autocorrelation functions. The study showed that the critical slip surface geometry and the probability of failure can be significantly different when the anisotropy of spatial correlation is accounted for. The study also showed that neglecting spatial correlation may over-estimate the probability of failure, however this finding was noted to be likely case-specific. The primary conclusion of the study was that appropriate representation of spatial correlation is essential to calculating the probability of failure. Finally, convergence of the probabilistic simulation was evaluated using bootstrapping of the simulated factor of safety distribution to assess the standard error in the mean factor of safety, standard deviation of factor of safety and the probability of failure. A convergence criterion based on the percentage standard error in the probability of failure was proposed and used to define the number of Monte-Carlo iterations required.

<p>Previous papers have elaborated formal gradient analysis for spatial processes, focusing on the distribution theory for directional derivatives associated with a response variable assumed to follow a Gaussian process model. In the current work, these ideas are extended to additionally accommodate one or more continuous covariate(s) whose directional derivatives are of interest and to relate the behavior of the directional derivatives of the response surface to those of the covariate surface(s). It is of interest to assess whether, in some sense, the gradients of the response follow those of the explanatory variable(s), thereby gaining insight into the local relationships between the variables. The joint Gaussian structure of the spatial random effects and associated directional derivatives allows for explicit distribution theory and, hence, kriging across the spatial region using multivariate normal theory. The gradient analysis is illustrated for bivariate and multivariate spatial models, non-Gaussian responses such as presence-absence and point patterns, and outlined for several additional spatial modeling frameworks that commonly arise in the literature. Working within a hierarchical modeling framework, posterior samples enable all gradient analyses to occur as post model fitting procedures.</p> / Dissertation

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

One of the main classes of spatial epidemiological studies is disease mapping, where the main aim is to describe the overall disease distribution on a map, for example, to highlight areas of elevated or lowered mortality or morbidity risk, or to identify important social or environmental risk factors adjusting for the spatial distribution of the disease. This thesis focused and proposed solutions to two most commonly seen obstacles in disease mapping applications, the changing census boundaries due to long study period and data aggregation for patients' confidentiality. In disease mapping, when target diseases have low prevalence, the study usually covers a long time period to accumulate sufficient cases. However, during this period, numerous irregular changes in the census regions on which population is reported may occur, which complicates inferences. A new model was developed for the case when the exact location of the cases is available, consisting of a continuous random spatial surface and fixed effects for time and ages of individuals. The process is modelled on a fine grid, approximating the underlying continuous risk surface with Gaussian Markov Random Field and Bayesian inference is performed using integrated nested Laplace approximations. The model was applied to clinical data on the location of residence at the time of diagnosis of new Lupus cases in Toronto, Canada, for the 40 years to 2007, with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are produced for Lupus and, for comparison, Psoriatic Arthritis data from the same clinic. Simulation studies are also carried out to better understand the performance of the proposed model as well as to compare with existing methods. When the exact locations of the cases are not known, inference is complicated by the uncertainty of case locations due to data aggregation on census regions for confidentiality. Conventional modelling relies on the census boundaries that are unrelated to the biological process being modelled, and may result in stronger spatial dependence in less populated regions which dominate the map. A new model was developed consisting of a continuous random spatial surface with aggregated responses and fixed covariate effects on census region levels. The continuous spatial surface was approximated by Markov random field, greatly reduces the computational complexity. The process was modelled on a lattice of fine grid cells and Bayesian inference was performed using Markov Chain Monte Carlo with data augmentation. Simulation studies were carried out to assess performance of the proposed model and to compare with the conventional Besag-York-Molli\'e model as well as model assuming exact locations are known. Receiver operating characteristic curves and Mean Integrated Squared Errors were used as measures of performance. For the application, surveillance data on the locations of residence at the time of diagnosis of syphilis cases in North Carolina, for the 9 years to 2007 are modelled with the aim of finding areas of abnormally high risk. Predicted risk surfaces and posterior exceedance probabilities are also produced, identifying Lumberton as a ``syphilis hotspot".

Characterization of carbonate reservoirs by flow units is a practical way of reservoir zonation. This study represents a petrophysical-based method that uses well loggings and core plug data to delineate flow units within the most productive carbonate reservoir of Derdere Formation in Y field, Southeast Turkey. Derdere Formation is composed of limestones and dolomites. Logs from the 5 wells are the starting point for the reservoir characterization. The general geologic framework obtained from the logs point out for discriminations within the formation. 58 representative core plug data from 4 different wells are utilized to better understand the petrophysical framework of the formation. The plots correlating petrophysical parameters and the frequency histograms suggest the presence of distinctive reservoir trends. These discriminations are also represented in Winland porosity-permeability crossplots resulted in clusters for different port-sizes that are responsible for different flow characteristics. Although the correlation between core plug porosity and air permeability yields a good correlation coefficient, the formation has to be studied within units due to differences in port-sizes and reservoir process speed. Linear regression and multiple regression analyses are used for the study of each unit. The results are performed using STATGRAPH Version Plus 5.1 statistical software. The permeability models are constructed and their reliabilities are compared by the regression coefficients for predictions in un-cored sections. As a result of this study, 4 different units are determined in the Derdere Formation by using well logging data, and core plug analyses with the help of geostatistical methods. The predicted permeabilities for each unit show good correlations with the calculated ones from core plugs. Highly reliable future estimations can be based on the derived methods.

O presente trabalho teve por objetivo quantificar e caracterizar a variabilidade espacial e temporal da armazenagem de água no solo, do potencial mátrico, densidade de fluxo e evapotranspiração da água em um Latossolo Vermelho Amarelo Argissólico cultivado com citros. A parcela experimental constitui-se de duas transeções com 20 pontos de observação espaçados de 4,0 m, cada um deles localizado no centro da distância entre duas plantas ao longo da linha. A cultura de citros foi implantada em março de 1991 com espaçamento de 4,0 m entre plantas e 7,0 m entre linhas. Em cada ponto de observação foram instalados a) um tubo de acesso à uma sonda de nêutrons até a profundidade de 1,20 m, para a quantificação da armazenagem da água no solo, e b) três tensiômetros nas profundidades de 1,00 m, 1,10 m e 1,20 m para a quantificação do potencial mátrico e do gradiente de potencial total da água no solo. As medidas foram feitas ao longo de três anos. As medidas dos potenciais mátricos foram realizadas diariamente e as da armazenagem semanalmente. De cada ponto de observação e profundidade de 0,30 m, 0,50 m, 0,70 m, 0,90 m e 1,10 m foram coletadas amostras de solo com estrutura indeformada para a determinação da condutividade hidráulica saturada, da curva de retenção e a densidade do solo. Por meio da geoestatística, verificou-se uma dependência espacial para a armazenagem de água no solo com um alcance de 17,0 m em média e, para a densidade do solo, potencial mátrico e condutividade hidráulica saturada não se verificou estrutura de dependência espacial. A técnica da estabilidade temporal possibilita identificar no campo o ponto ou os pontos que, subestimam, superestimam ou representam a média de uma determinada variável. Pelo coeficiente de correlação de Spearman entre as datas, os valores de armazenagem e de potencial mátrico foram estáveis no tempo; para a armazenagem de água no solo, a correlação foi maior no período de recarga e, para o potencial mátrico, os coeficientes de correlação foram maiores para os períodos de secagem, ou seja, a estabilidade temporal foi maior para o os períodos de secagem. Por meio da diferença relativa foi possível identificar no estudo da estabilidade temporal, os pontos que mais se aproximaram da média que foram: o ponto 29 nos três anos, para a armazenagem de água no solo, pontos 24, 13 e 11 para o potencial mátrico da água no solo nos anos 1, 2 e 3, respectivamente. Por meio do balanço hídrico, verificou-se um consumo da água pela cultura de citros (evapotranspiração real) de 1.340 mm (consumo médio diário de 3,49 mm) e uma perda por drenagem interna que correspondeu a 10 %, em média nos três anos, da precipitação, para o solo e cultura em estudo. / The objective of the present work was to quantify and characterize spatial and temporal variability of water storage, matric potential, water flux density and actual evapotranspiration in an oxisol cropped to citrus. The experimental plot consisted of two transects with 20 observation points in a spacing of four meters. Each observation point was located in the central distance between two plants in the plant line. The citrus crop was installed in March of 1991 in a spacing of 4 x 7 m (four meters among plants in the line and seven meters among lines). In each observation point were installed a) one aluminiun tube to access a neutron probe till the depth of 1.2 m to determine water storage in the 0-1.2 m soil profile and b) three tensiometers at the depths of 1.0, 1.1 and 1.2 m to determine soil matric potential and soil water total potential gradient at the depth of 1.1 m then to calculated soil water flux density at the 1.1 m soil depth by means of Darcy-Buckingham equation. Measurements were made during three years, those of matric potential, dayly, and those of soil water content, weekly. In each observation point, undisturbed soil samples were removed from 0.3, 0.5, 0.7, 0.9 e 1.1 m soil depths to determine saturated soil hydraulic conductivity, soil water retention curve and soil bulk density. By means of geostatistical techniques, spatial dependence for soil water storage was verified with a range of 17 m in average. It was observed time stability for both soil water storage and matric potential that from the Spearman rank coefficients among dates. For the soil water storage, this coefficient showed higher values during the recharge period, that this, time stability for soil water storage was higher during the recharge period. For the matric potential occurred the inverse: time stability was higher during the drying period. By means of the relative difference from the mean, in the time stability study the following points closer were to the mean: point 29 (for the three years) for soil water storage and points 24, 13 and 11 for soil water matric potential for years 1, 2 and 3, respectively. By means of the soil water storage methodology, it was quantified a water consumption (actual evapotranspiration) for the citrus crop of 1,340 mm (daily average value of 3.49 mm) and a water loss by internal drainage that corresponded to 10 % in average, for the three years of experiment.