Natural attenuation refers to the observed reduction in contaminant concentration via natural processes as contaminants migrate from the source into environmental media. Assessment of the dimensions of contaminant plumes and prediction of their fate requires predictions of the rate of dissolution of contaminants from residual non-aqueous-phase liquids (NAPLs) into the aquifer and the rate of contaminant removal through biodegradation. The available techniques to estimate these parameters do not characterize their confidence intervals by accounting for their relationships to uncertainty in source geometry and hydraulic conductivity distribution. The central idea in this thesis is to develop a flexible modeling approach for characterization of uncertainty in residual NAPL dissolution rate and first-order biodegradation rate by tailoring the estimation of these parameters to distributions of uncertainty in source size and hydraulic conductivity field. The first development in this thesis is related to a distance function approach that characterizes the uncertainty in the areal limits of the source zones. Implementation of the approach for a given monitoring well arrangement results in a unique uncertainty band that meets the requirements of unbiasedness and fairness of the calibrated probabilities. The second development in this thesis is related to a probabilistic model for characterization of uncertainty in the 3D localized distribution of residual NAPL in a real site. A categorical variable is defined based on the available CPT-UVIF data, while secondary data based on soil texture and groundwater table elevation are also incorporated into the model. A cross-validation study shows the importance of incorporation of secondary data in improving the prediction of contaminated and uncontaminated locations. The third development in this thesis is related to the implementation of a Monte Carlo type inverse modeling to develop a screening model used to characterize the confidence intervals in the NAPL dissolution rate and first-order biodegradation rate. The development of the model is based on sequential self-calibration approach, distance-function approach and a gradient-based optimization. It is shown that tailoring the estimation of the transport parameters to joint realizations of source geometry and transmissivity field can effectively reduce the uncertainties in the predicted state variables.

Many geological deposits contain nonlinear anisotropic features such as veins, channels, folds or local changes in orientation; numerical property modeling must account for these features to be reliable and predictive. This work incorporates locally varying anisotropy into inverse distance estimation, kriging and sequential Gaussian simulation. The methodology is applicable to a range of fields including (1) mining-mineral grade modeling (2) petroleum-porosity, permeability, saturation and facies modeling (3) environmental-contaminate concentration modeling. An exhaustive vector field defines the direction and magnitude of anisotropy and must be specified prior to modeling. Techniques explored for obtaining this field include: manual; moment of inertia of local covariance maps; direct estimation and; automatic feature interpolation. The methodology for integrating locally varying anisotropy into numerical modeling is based on modifying the distance/covariance between locations in space. Normally, the straight line path determines distance but in the presence of nonlinear features the appropriate path between locations traces along the features. These paths are calculated with the Dijkstra algorithm and may be nonlinear in the presence of locally varying anisotropy. Nonlinear paths do not ensure positive definiteness of the required system of equations when used with kriging or sequential Gaussian simulation. Classical multidimensional scaling is applied to ensure positive definiteness but is found to be computationally infeasible for large models, thus, landmark points are used for efficiency with acceptable losses in precision. The methodology is demonstrated on two data sets (1) net thickness of the McMurray formation in northern Alberta and (2) gold grade in a porphyry copper deposit. Integrating LVA into numerical modeling increases local accuracy and improves leave-one-out cross validation analysis results in both case studies. / Mining Engineering

During the last thirty years, new technologies have contributed to a drastic increase of the amount of data in environmental sciences. Monitoring networks, remote sensors, archived maps and large databases are just few examples of the possible information sources responsible for this growing amount of information. For obvious reasons, it might be interesting to account for all these information when dealing with a space-time prediction/estimation context. In environmental sciences, measurements are very often sampled scarcely over space and time. Geostatistics is the field that investigates variables in a space-time context. It includes a large number of methods and approaches that all aim at providing space-time predictions (or interpolations) for variables scarcely known in space and in time by accounting for space-time dependance between these variables. As a consequence, geostatistics methods are relevant when dealing with the processing and the analysis of environmental variables in which space and time play an important role. As direct consequence of the increasing amount of data, there is an important diversity in the information (e.g. different nature, different uncertainty). These issues have recently motivated the emergence of the concept of data fusion. Broadly speaking, the main objective of data fusion methods is to deal with various information sources in such a way that the final result is a single prediction that accounts for all the sources at once. This enables thus to conciliate several and potentially contradictory sources instead of having to select only one of them because of a lack of appropriate methodology. For most of existing geostatistics methods, it is quite difficult to account for a potentially large number of different information sources at once. As a consequence, one has often to opt for only one information source among all the available sources. This of course leads to a dramatic loss of information. In order to avoid such choices, it is thus relevant to get together the concepts of both data fusion and geostatistics in the context of environmental sciences. The objectives of this thesis are (i) to develop the theory of a Bayesian data fusion (BDF) framework in a space-time prediction context and (ii) to illustrate how the proposed BDF framework can account for a diversity of information sources in a space-time context. The method will thus be applied to a few environmental sciences applications for which (i) crucial available information sources are typically difficult to account for or (ii) the number of secondary information sources is a limitation when using existing methods. Reproduced by permission of Springer. P. Bogaert and D. Fasbender (2007). Bayesian data fusion in a spatial prediction context: a general formulation. Stoch. Env. Res. Risk. A., vol. 21, 695-709. (Chap. 1). © 2008 IEEE. Reprinted, with permission, from D. Fasbender, J. Radoux and P. Bogaert (2008). Bayesian data fusion for adaptable image pansharpening. IEEE Trans. Geosci. Rem. Sens., vol. 46, 1847-1857. (Chap. 3). © 2008 IEEE. Reprinted, with permission, from D. Fasbender, D. Tuia, P. Bogaert and M. Kanevski (2008). Support-based implementation of Bayesian data fusion for spatial enhancement: applications to ASTER thermal images. IEEE Geosci. Rem. Sens. Letters, vol. 6, 598-602. (Chap. 4). Reproduced by permission of American Geophysical Union. D. Fasbender, L. Peeters, P. Bogaert and A. Dassargues (2008). Bayesian data fusion applied to water table spatial mapping. Accepted for publication in Water Resour. Res. (Chap. 5).

As land-cover conversion continues to expand into ever more remote areas in the humid tropics, montane rainforests are increasingly threatened. In the south Ecuadorian Andes, they are not only subject to man-made disturbances but also to naturally occurring landslides. I was interested in the impact of this ecosystem dynamics on a key parameter of the hydrologic cycle, the soil saturated hydraulic conductivity (synonym: permeability; Ks from here on), because it is a sensitive indicator for soil disturbances. My general objective was to quantify the effects of the regional natural and human disturbances on the saturated hydraulic conductivity and to describe the resulting spatial-temporal patterns. The main hypotheses were: 1) disturbances cause an apparent displacement of the less permeable soil layer towards the surface, either due to a loss of the permeable surface soil after land-sliding, or as a consequence of the surface soil compaction under cattle pastures; 2) ‘recovery’ from disturbance, either because of landslide re-vegetation or because of secondary succession after pasture abandonment, involves an apparent displacement of the less permeable layer back towards the original depth an 3) disturbances cause a simplification of the Ks spatial structure, i.e. the spatially dependent random variation diminishes; the subsequent recovery entails the re-establishment of the original structure. In my first study, I developed a synthesis of recent geostatistical research regarding its applicability to soil hydraulic data, including exploratory data analysis and variogram estimation techniques; I subsequently evaluated the results in terms of spatial prediction uncertainty. Concerning the exploratory data analysis, my main results were: 1) Gaussian uni- and bivariate distributions of the log-transformed data; 2) the existence of significant local trends; 3) no need for robust estimation; 4) no anisotropic variation. I found partly considerable differences in covariance parameters resulting from different variogram estimation techniques, which, in the framework of spatial prediction, were mainly reflected in the spatial connectivity of the Ks-field. Ignoring the trend component and an arbitrary use of robust estimators, however, would have the most severe consequences in this respect. Regarding variogram modeling, I encouraged restricted maximum likelihood estimation because of its accuracy and independence on the selected lags needed for experimental variograms. The second study dealt with the Ks spatial-temporal pattern in the sequences of natural and man-made disturbances characteristic for the montane rainforest study area. To investigate the disturbance effects both on global means and the spatial structure of Ks, a combined design-and model-based sampling approach was used for field-measurements at soil depths of 12.5, 20, and 50 cm (n=30-150/depth) under landslides of different ages (2 and 8 years), under actively grazed pasture, fallows following pasture abandonment (2 to 25 years of age), and under natural forest. Concerning global means, our main findings were 1) global means of the soil permeability generally decrease with increasing soil depth; 2) no significant Ks differences can be observed among landslides and compared to the natural forest; 3) a distinct permeability decrease of two orders of magnitude occurs after forest conversion to pasture at shallow soil depths, and 4) the slow regeneration process after pasture abandonment requires at least one decade. Regarding the Ks spatial structure, we found that 1) disturbances affect the Ks spatial structure in the topsoil, and 2) the largest differences in spatial patterns are associated with the subsoil permeability. In summary, the regional landslide activity seems to affect soil hydrology to a marginal extend only, which is in contrast to the pronounced drop of Ks after forest conversion. We used this spatial-temporal information combined with local rain intensities to assess the partitioning of rainfall into vertical and lateral flowpaths under undisturbed, disturbed, and regenerating land-cover types in the third study. It turned out that 1) the montane rainforest is characterized by prevailing vertical flowpaths in the topsoil, which can switch to lateral directions below 20 cm depth for a small number of rain events, which may, however, transport a high portion of the annual runoff; 2) similar hydrological flowpaths occur under the landslides except for a somewhat higher probability of impermeable layer formation in the topsoil of a young landslide, and 3) pronounced differences in runoff components can be observed for the human disturbance sequence involving the development of near-surface impeding layers for 24, 44, and 8 % of rain events for pasture, a two-year-old fallow, and a ten-year-old fallow, respectively. / Der tropische Bergregenwald in den Südecuadorianischen Anden unterliegt sowohl anthropogenen Eingriffen, d.h. der Umwandlung von Naturwald in Rinderweiden, als auch natürlichen Störungen in der Form von Hangrutschen. Ziel meiner Arbeit war es, die Auswirkungen dieser regionalen Störungsdynamik auf einen Schlüsselparameter des hydrologischen Kreislaufs, die gesättigte hydraulische Wasserleitfähigkeit (Ks), zu untersuchen und die resultierenden raum-zeitlichen Muster zu beschreiben. In der ersten Studie habe ich eine Synthese aktueller geostatistischer Forschung hinsichtlich ihrer Eignung für die Analyse bodenhydrologischer Daten entwickelt. Diese beinhaltet explorative Datenanalyse und verschiedene Techniken zur Schätzung der Kovarianzparameter; die Ergebnisse habe ich in Bezug auf die Ungenauigkeit räumlicher Vorhersagen bewertet. Es hat sich dabei herausgestellt, dass die Schätztechniken teilweise beachtliche Unterschiede in den Parametern hervorrufen, welche sich hauptsächlich in der räumlichen Konnektivität widergespiegeln. Die wichtigste Rolle im Zusammenhang mit der räumlichen Vorhersage kommt jedoch den vorgeordneten explorativen Analyseschritten zu. In der zweiten Studie habe ich mich mit der Beschreibung des raum-zeitlichen Muster der Wasserleitfähigkeit in den anthropogenen und natürlichen Störungsgradienten beschäftigt. Wichtigste Ergebnisse waren, dass es keine signifikanten Unterschiede der Wasserleitfähigkeit zwischen den verschieden alten Hangrutschen und dem Naturwald gibt. Daraus lässt sich schließen, dass die natürlichen Störungen im Untersuchungsgebiet lediglich marginale Auswirkungen auf die Bodenhydrology haben. Das steht in starkem Kontrast zum anthropogenen Störungskreislauf: die Wasserleitfähigkeit im Weideboden hat gegenüber dem Naturwald um zwei Größenordnungen abgenommen; eine „Erholung“ nach Nutzungsaufgabe scheint mindestens ein Jahrzehnt in Anspruch zu nehmen. Die räumlichen Abhängigkeit von Ks in den Oberböden von Wald und einer alten Brache ist stärker als in jenen der gestörten Flächen, was auf eine störungsbedingte Beeinträchtigung der räumlichen Struktur in geringer Bodentiefe schließen lässt. In der dritten Studie habe ich diese raum-zeitlichen Informationen mit dem örtlichen Niederschlagsregime in Verbindung gebracht, um Rückschlüsse auf die Auswirkungen der störungsbedingten Änderungen von Ks auf hydrologische Fließwege zu ziehen. Es hat sich gezeigt, dass im tropischen Bergregenwald und unter Hangrutschen ubiquitäre Tiefenversickerung dominiert, es allerdings zu einer Verschiebung in laterale Fließrichtungen für die seltenen intensiven Regenereignisse kommen kann. Anthropogene Störungen gehen mit einer um bis zu 50 Prozent erhöheren Wahrscheinlichkeit des Auftretens oberflächennaher Stauschichten einher, was die Bedeutung lateraler Fließwege erhöht. Dies trifft in vergleichbarer Größenordnung auch auf ein Vergleichsökosystem im Tieflandregenwald zu.

Motivations and research objectives: During the passage of rain water through a forest canopy two main processes take place. First, water is redistributed; and second, its chemical properties change substantially. The rain water redistribution and the brief contact with plant surfaces results in a large variability of both throughfall and its chemical composition. Since throughfall and its chemistry influence a range of physical, chemical and biological processes at or below the forest floor the understanding of throughfall variability and the prediction of throughfall patterns potentially improves the understanding of near-surface processes in forest ecosystems. This thesis comprises three main research objectives. The first objective is to determine the variability of throughfall and its chemistry, and to investigate some of the controlling factors. Second, I explored throughfall spatial patterns. Finally, I attempted to assess the temporal persistence of throughfall and its chemical composition. Research sites and methods: The thesis is based on investigations in a tropical montane rain forest in Ecuador, and lowland rain forest ecosystems in Brazil and Panama. The first two studies investigate both throughfall and throughfall chemistry following a deterministic approach. The third study investigates throughfall patterns with geostatistical methods, and hence, relies on a stochastic approach. Results and Conclusions: Throughfall is highly variable. The variability of throughfall in tropical forests seems to exceed that of many temperate forests. These differences, however, do not solely reflect ecosystem-inherent characteristics, more likely they also mirror management practices. Apart from biotic factors that influence throughfall variability, rainfall magnitude is an important control. Throughfall solute concentrations and solute deposition are even more variable than throughfall. In contrast to throughfall volumes, the variability of solute deposition shows no clear differences between tropical and temperate forests, hence, biodiversity is not a strong predictor of solute deposition heterogeneity. Many other factors control solute deposition patterns, for instance, solute concentration in rainfall and antecedent dry period. The temporal variability of the latter factors partly accounts for the low temporal persistence of solute deposition. In contrast, measurements of throughfall volume are quite stable over time. Results from the Panamanian research site indicate that wet and dry areas outlast consecutive wet seasons. At this research site, throughfall exhibited only weak or pure nugget autocorrelation structures over the studies lag distances. A close look at the geostatistical tools at hand provided evidence that throughfall datasets, in particular those of large events, require robust variogram estimation if one wants to avoid outlier removal. This finding is important because all geostatistical throughfall studies that have been published so far analyzed their data using the classical, non-robust variogram estimator. / Motivation und Zielsetzung: Wenn Regen durch ein Kronendach fällt lassen sich zwei Prozesse beobachten: das Regenwasser wird umverteilt und die chemische Qualität des Wassers verändert sich erheblich. Die Prozesse im Kronenraum resultieren in einer hohen Variabilität des Bestandsniederschlags und dessen chemischer Zusammensetzung. Bestandsniederschlag beeinflusst eine Reihe von physikalischen, chemischen und biologischen Prozessen am Waldboden. Daher können Untersuchungen zur Variabilität und zu Mustern im Bestandsniederschlag helfen, bodennahe Prozesse besser zu verstehen. Diese Dissertation behandelt hauptsächlich drei Aspekte. Erstens, die Arbeit beschäftigt sich mit der Erfassung der Variabilität im Bestandsniederschlag und dessen chemischer Zusammensetzung, zudem werden Einflussfaktoren dieser Variabilität untersucht. Des Weiteren beschäftigt sich die Arbeit mit räumlichen Mustern des Bestandsniederschlagswassers, und drittens wird die zeitliche Stabilität des Bestandsniederschlags und dessen chemischer Zusammensetzung betrachtet. Untersuchungsgebiete und Methoden: Diese Dissertation basiert auf Untersuchungen in einem tropischen Bergregenwald in Ecuador, sowie Studien in tropischen Tieflandregenwäldern in Brasilien und Panama. Die ersten zwei Studien untersuchen Bestandsniederschlag und dessen chemische Zusammensetzung mit Hilfe deterministischer Methoden. Die Arbeit in Panama nutzt geostatistische Methoden zur Beschreibung von Bestandsniederschlagsmustern und verfolgt somit einen stochastischen Ansatz. Ergebnisse und Schlussfolgerungen: Die Variabilität des Bestandsniederschlages ist hoch; das heißt, die Menge des auf den Waldboden tropfenden Wassers kann sich je nach Standort stark unterscheiden. Diese räumliche Variabilität des Bestandsniederschlags ist in tropischen Wäldern höher als in vielen gemäßigten Waldökosystemen, was nicht allein auf verschiedenen Eigenschaften der Ökosysteme zurückzuführen ist. Vielmehr erklären sich die Unterschiede auch aus verschiedenen Waldnutzungen. Abgesehen von biologischen Faktoren beeinflusst die Regenmenge die Variabilität des Bestandsniederschlags erheblich. Die chemische Zusammensetzung des Bestandsniederschlags weist eine noch höhere Variabilität als der Bestandsniederschlag selbst auf. Unterschiede zwischen tropischen und gemäßigten Wäldern lassen sich hier allerdings nicht erkennen, weshalb die hohe Diversität tropischer Ökosysteme die Heterogenität der chemischen Zusammensetzung des Bestandsniederschlags nicht ausreichend erklärt. Eine Vielzahl anderer Faktoren kontrolliert deshalb die Variabilität der Bestandsniederschlagschemie, beispielsweise die Konzentration gelöster Stoffe im Regenwasser oder die Dauer von Trockenperioden. Deren hohe temporale Variabilität ist verantwortlich für die geringe zeitliche Stabilität von Depositionsmessungen. Im Gegensatz dazu ist die temporale Persistenz von Messungen der Bestandsniederschlagsmenge hoch. Insbesondere die Ergebnisse aus Panama zeigen, dass feuchte und trockene Messpunkte über einen Zeitraum von zwei Regenzeiten fortbestehen. Die räumlichen Bestandsniederschlagsmuster im letztgenannten Untersuchungsgebiet sind schwach bzw. weisen die Struktur eines reinen Nugget-Models auf. Die geostatistische Analyse zeigt, dass vor allem die Daten großer Regenereignisse eine robuste Modellierung des Variogramms erfordern, wenn die willkürliche Entfernung von Fernpunkten in den Daten vermieden werden soll. Dieses Resultat ist insbesondere deshalb von Bedeutung, da alle bisherigen Bestandsniederschlagsstudien den klassischen, nicht-robusten Schätzer benutzen, obwohl das Auftreten von Extremwerten in Bestandsniederschlagsdaten für viele Ökosysteme zu erwarten ist.

Inhomogeneities in the earth (fractures, layering, shape, composition) are responsible for seismic wave scattering and contribute towards amplitude, travel time, frequency and spectral fluctuations observed in seismic records. This thesis presents findings that complement our understanding of seismic scattering and imaging in heterogeneous media. Interest focused on probing the correlation between spatial variations in attributes that characterize the state (physical, chemical) of rocks and seismic waveform data with consideration towards potential implications for seismic survey design to optimize imaging, imaging with converted waves, microseismic monitoring, velocity modeling and imaging of lithological boundaries. The highlights of the research strategy include: • The use of stochastic methods to build realistic earth models that characterize the 1D, 2D and 3D spatial variations in rock properties. These petrophysical earth models are conditioned by experimental (“hard”) data such as geology, wave velocities and density from case study areas like the Bosumtwi impact crater and the base metal deposits in Nash Creek (Canada) and Thompson (Canada). The distributions of the sulfide mineralization at Nash Creek and at Thompson represent two end members of the heterogeneity spectrum. While the sulfide mineralization at Nash Creek is highly disseminated in nature, the sulfide rich zones at Thompson occur as well defined volumes (lens-shaped) having a strong density contrast with respect to the host rocks. • Analysis of modeled forward (transmitted) and backward scattered wave propagation in the heterogeneous earth models. As a result of a study aimed at correlating resonant frequencies to scale length parameters, it is observed that the efficiency of the spectral ratio method is undermined by its sensitivity to the interference between P- and S-waves as well as the impedance contrast. It is also demonstrated that travel time of direct arrivals (transmitted waves) can be used to infer structural heterogeneity and velocity distribution beyond borehole locations. However, the success of imaging with transmitted waves is subject to the influence of geology which must factor in the choice of acquisition geometry. For the first time, multivariate and multidimensional (3D) heterogeneous earth models that are conditioned by hard data from multiple boreholes are constructed. The methodology requires having at least one physical rock property attribute that is sampled along the whole borehole length. This approach helped to characterize the uncertainty in the distribution of rock densities and metal content in a study region of the Nash Creek property. The density data suggests the sulfides are disseminated and this poses challenges for both gravity and seismic imaging methods. Modeling studies suggest seismic methods will not be suited for imaging zones with such disseminated mineralization. On the other hand, when dealing with massive sulfide mineralization that has complex geology (steep dip) like the case in Thompson, the success of the seismic imaging process relies very much on the acquisition geometry as well as the variability of the physical properties of the host rock. Elastic modeling results show that a Vertical Seismic Profiling (VSP) geometry is better suited to capture the down-dip scattered wavefield from the orebody. While surface acquisition geometry with sufficient extended length in the down dip direction can also be used to detect the dipping orebody, its efficiency can however be undermined by background heterogeneity: when the scale length along the direction of dip is comparable to the dimensions of the orebody, the scattered wavefields are strong enough to mask the diffraction hyperbola generated from the ore. Moreover, the study also corroborates that converted waves generated from the scattering processes hold promise as an imaging tool for a dipping orebody as they are least affected by the scattering processes of background heterogeneity.

Inhomogeneities in the earth (fractures, layering, shape, composition) are responsible for seismic wave scattering and contribute towards amplitude, travel time, frequency and spectral fluctuations observed in seismic records. This thesis presents findings that complement our understanding of seismic scattering and imaging in heterogeneous media. Interest focused on probing the correlation between spatial variations in attributes that characterize the state (physical, chemical) of rocks and seismic waveform data with consideration towards potential implications for seismic survey design to optimize imaging, imaging with converted waves, microseismic monitoring, velocity modeling and imaging of lithological boundaries. The highlights of the research strategy include: • The use of stochastic methods to build realistic earth models that characterize the 1D, 2D and 3D spatial variations in rock properties. These petrophysical earth models are conditioned by experimental (“hard”) data such as geology, wave velocities and density from case study areas like the Bosumtwi impact crater and the base metal deposits in Nash Creek (Canada) and Thompson (Canada). The distributions of the sulfide mineralization at Nash Creek and at Thompson represent two end members of the heterogeneity spectrum. While the sulfide mineralization at Nash Creek is highly disseminated in nature, the sulfide rich zones at Thompson occur as well defined volumes (lens-shaped) having a strong density contrast with respect to the host rocks. • Analysis of modeled forward (transmitted) and backward scattered wave propagation in the heterogeneous earth models. As a result of a study aimed at correlating resonant frequencies to scale length parameters, it is observed that the efficiency of the spectral ratio method is undermined by its sensitivity to the interference between P- and S-waves as well as the impedance contrast. It is also demonstrated that travel time of direct arrivals (transmitted waves) can be used to infer structural heterogeneity and velocity distribution beyond borehole locations. However, the success of imaging with transmitted waves is subject to the influence of geology which must factor in the choice of acquisition geometry. For the first time, multivariate and multidimensional (3D) heterogeneous earth models that are conditioned by hard data from multiple boreholes are constructed. The methodology requires having at least one physical rock property attribute that is sampled along the whole borehole length. This approach helped to characterize the uncertainty in the distribution of rock densities and metal content in a study region of the Nash Creek property. The density data suggests the sulfides are disseminated and this poses challenges for both gravity and seismic imaging methods. Modeling studies suggest seismic methods will not be suited for imaging zones with such disseminated mineralization. On the other hand, when dealing with massive sulfide mineralization that has complex geology (steep dip) like the case in Thompson, the success of the seismic imaging process relies very much on the acquisition geometry as well as the variability of the physical properties of the host rock. Elastic modeling results show that a Vertical Seismic Profiling (VSP) geometry is better suited to capture the down-dip scattered wavefield from the orebody. While surface acquisition geometry with sufficient extended length in the down dip direction can also be used to detect the dipping orebody, its efficiency can however be undermined by background heterogeneity: when the scale length along the direction of dip is comparable to the dimensions of the orebody, the scattered wavefields are strong enough to mask the diffraction hyperbola generated from the ore. Moreover, the study also corroborates that converted waves generated from the scattering processes hold promise as an imaging tool for a dipping orebody as they are least affected by the scattering processes of background heterogeneity.

In this dissertation, 3D surface seismic data was integrated with the well logs to be able to define the properties in every location for the reservoir under investigation. To accomplish this task, geostatistical and artificial neural networks (ANN) techniques were employed. First, missing log sets in the study area were estimated using common empirical relationships and ANN. Empirical estimations showed linear dependent results that cannot be generalized. On the other hand, ANNs predicted missing logs with an very high accuracy. Sonic logs were predicted using resistivity logs with 90% correlation coefficient. Second, acoustic impedance property was predicted in the study area. AI estimation first performed using sonic log with GRNN and 88% CC was obtained. AI estimation was repeated using sonic and resistivity logs and the result were improved to 94% CC. In the final part of the study, SGS technique was used with collocated cokriging techniques to estimate NPHI property. Results were varying due to nature of the algorithm. Then, GRNN and RNN algorithms were applied to predict NPHI property. Using optimized GRNN network parameters, NPHI was estimated with high accuracy. Results of the study were showed that ANN provides a powerful solution for reservoir parameter prediction in the study area with its flexibility to find out nonlinear relationships from the existing available data.

<p>In this study, five real-life problem situations were used to explore the challenges of developing and implementing decision support tools for management of an urban water utility in Uganda. The study sought to explore how the degree of adoption of formal decision support tools in practice, generally perceived to be low, could be improved. In the study, an Action Research (AR) approach was used. AR is an inquiry process that involves partnership between researchers and practitioners for the purpose of addressing a real-life problem issue, while simultaneously gener-ating scientific knowledge. Unlike other research methods where the researcher seeks to study organizational phenomena but not to change them, the action researcher attempts to create or-ganizational change and simultaneously to study the process. It is recognized that AR methods provide a potential avenue to improve the practical relevance of Information Systems (IS) re-search.</p><p>The five cases that were considered in the study involved participatory problem structuring to address water distribution bottlenecks; identification of Non-Revenue Water (NRW) reduction strategies; facilitation of decentralized management of customer accounts; monitoring and con-trol of procurements and expenditure; and geospatial investigation of declining water sales. Dur-ing the study, participation in problem identification was achieved through discussions and brain-storming sessions bringing together top and middle managers within the organization. A number of prototype decision support tools were developed and implemented. Maps and other geovisu-alization tools were also used to inform and enhance the processes of collective problem identifi-cation and structuring.</p><p>Results of the study emphasized the need for proper problem structuring prior to the formula-tion of actions; the challenge of moving from planning to action; the importance of user in-volvement in the development of tools; and the need to manage IS implementation as part of a holistic, organization-wide change process. The challenges of embedding formal decision support within existing work systems in organizations were highlighted, and recommendations were made on how best to achieve this. The AR approach was found to be useful in bridging the gap be-tween academic research and technological practice, thus supporting the development of IS with immediate and practical benefits to organizations.</p>

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