258 research outputs found

    A review of earth-viewing methods for in-flight assessment of modulation transfer function and noise of optical spaceborne sensors

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    Several earth observation satellites bear optical imaging sensors whose outputs are essential in many environmental aspects. This paper focuses on two parameters of the quality of the imaging system: the Modulation Transfer Function (MTF) and Signal to Noise Ratio (SNR). These two parameters evolve in time and should be periodically monitored in-flight to control the quality of delivered images and possibly mitigate defaults. Only a very limited number of past and current sensors have an on-board calibration device fully appropriate to the assessment of the noise and none of them has capabilities for MTF assessment. Most often, vicarious techniques should be employed which are based on the Earth-viewing approach: an image, or a combination of images, is selected because the landscape offers certain properties, e.g., well-marked contrast or on the contrary, spatial homogeneity, whose knowledge or modeling permit the assessment of these parameters. Several methods have been proposed to perform in-flight assessments. This paper proposes a review of the principles and techniques employed in this domain

    Development of a Grade Control Technique Optimizing Dilution and Ore Loss Trade-off in Lateritic Bauxite Deposits

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    This thesis focusses on the development of new techniques to improve the resource estimation of laterite-type bauxite deposits. Contributions of the thesis include (1) a methodology to variogram-free modelling of the ore boundaries using multiple-point statistics, (2) an approach to automate the parameter tuning process for multiple-point statistical algorithms and (3) a grade control technique to minimise the economic losses due to dilution and ore loss

    Effective Product Lifecycle Management: the role of uncertainties in addressing design, manufacturing and verification processes

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    The aim of this thesis is to use the concept of uncertainty to improve the effectiveness of Product Lifecycle Management (PLM) systems. Uncertainty is a rather new concept in PLM that has been introduced with the new technical language, drawn by ISO, to manage Geometrical Product Specification and Verification (GPS) in the challenging environment of modern manufacturing. GPS standards regard in particular design and verification environments, and want to guarantee consistence of information through a technical language which define both specification and verification on sound logical and mathematical bases. In this context, uncertainty is introduced as the instrument that measures consistency: between the designer intentions (specifications) and the manufactured artefact (as it is observed through measurement) as well as between the measurand definition provided by designers (the specification again) and that used by metrologists. The implications of such an approach have been analyzed through a case study dealing with flatness tolerance and paying particular attention to the verification processes based on Coordinate Measuring Machines (CMM). A Design of Experiment (DoE) has been used and results have been analyzed and used to build a regression model that allows generalization in the experiment validity domain. Then, using Category Theory, a categorical data model has been defined which represents the operation based structure of GPS language and uses the flatness research results in order to design a software able to concretize the GPS vision of geometrical product specifications management. This software is able to translate specification requirements into verification instructions, estimate the uncertainty introduced by simplified verification operations and evaluate costs and risks of verification operations. It provides an important tool for designers, as it allows a responsible definition of specifications (designer can simulate the interpretation of specifications and have an idea of the costs related with their verification), and for metrologist, as it can be a guide for designing GPS compliant verification missions or handling the usual verification procedures according to the GPS standards. However, during the study, it has been matured the consciousness that this approach, even if correct and valuable, was not the most suitable to fully exploit the real potential of CMM. Then, aside the GPS oriented work, an adaptive sampling strategy, based on Kriging modelization, has been proposed with very encouraging result

    Variograms and kriging in the analysis of spatial data

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    This research is in the area of geostatistics and consists essentially of two parts. The first is an investigation of the variogram and cross variogram and the associated kriging and cokriging methods of spatial prediction and the second is an application of these in the analysis of two (original) data sets. In the first part (chapter 1 to chapter 5), the focus is on summarising and illustrating the various techniques of Exploratory Data Analysis (EDA) and some methods used to estimate and model the experimental variograms and cross variograms for a given data set, together with some of the geostatistical methods of kriging and cokriging used for prediction purposes. The research also illustrates some of the many applications of this theory in the earth and environmental sciences. The second part of the thesis (chapter 6) is an application of these geostatistical techniques to the analysis of two (new) data sets. The first data set consists of the Available Phosphate (in ppm) and Potassium (in ppm) from two fields (one cropped and one uncropped) in the Jimperding Brook area of Western Australia. The second data set consists of the number of species of Banksia at various locations within a region of Southwestern Australia

    Reservoir Characterization with Limited Sample Data using Geostatistics

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    The primary objective of this dissertation was to develop a systematic method to characterize the reservoir with the limited available data. The motivation behind the study was characterization of CO2 pilot area in the Hall Gurney Field, Lansing Kansas City Formation. The main tool of the study was geostatistics, since only geostatistics can incorporate data from variety of sources to estimate reservoir properties. Three different subjects in geostatistical methods were studied, analyzed and improved. The first part investigates the accuracy of different geostatistical methods as a function of the available sample data. The effect of number and type of samples on conventional and stochastical methods was studied using a synthetic reservoir. The second part of the research focuses on developing a systematic geostatistical method to characterize a reservoir in the case of very limited sample data. The objective in this part was the use of dynamic data, such as data from pressure transient analysis, in geostatistical methods. In the literature review of this part emphasis is given to those works involving the incorporation of well-test data and the use of simulated annealing to incorporate different type of static and dynamic data. The second part outlines a systematic procedure to estimate the reservoir properties for a CO2 pilot area in the Lansing Kansas City formation. The third part of the thesis discusses the multiple-point geostatistics and presents an improvement in reservoir characterization using training image construction. Similarity distance function is used to find the most consistent and similar pattern for to the existing data. This part of thesis presents a mathematical improvement to the existing similarity functions

    Geostatistical integration of geophysical, well bore and outcrop data for flow modeling of a deltaic reservoir analogue

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    Significant world oil and gas reserves occur in deltaic reservoirs. Characterization of deltaic reservoirs requires understanding sedimentary and diagenetic heterogeneity at the submeter scale in three dimensions. However, deltaic facies architecture is complex and poorly understood. Moreover, precipitation of extensive calcite cement during diagenesis can modify the depositional permeability of sandstone reservoir and affect fluid flow. Heterogeneity contributes to trapping a significant portion of mobile oil in deltaic reservoirs analogous of Cretaceous Frontier Formation, Powder River Basin, Wyoming. This dissertation focuses on 3D characterization of an ancient deltaic lobe. The Turonian Wall Creek Member in central Wyoming has been selected for the present study, which integrates outcrop digitized image analysis, 2D and 3D interpreted ground penetrating radar surveys, outcrop gamma ray measurements, well logs, permeameter logs and transects, and other data for 3D reservoir characterization and flow modeling. Well log data are used to predict the geological facies using beta-Bayes method and classic multivariate statistic methods, and predictions are compared with the outcrop description. Geostatistical models are constructed for the size, orientation, and shape of the concretions using interpreted GPR, well, and outcrop data. The spatial continuity of concretions is quantified using photomosaic derived variogram analysis. Relationships among GRP attributes, well data, and outcrop data are investigated, including calcite concretion occurrence and permeability measurements from outcrop. A combination of truncated Gaussian simulation and Bayes rule predicts 3D concretion distributions. Comparisons between 2D flow simulations based on outcrop observations and an ensemble of geostatistical models indicates that the proposed approach can reproduce essential aspects of flow behavior in this system. Experimental design, analysis of variance, and flow simulations examine the effects of geological variability on breakthrough time, sweep efficiency and upscaled permeability. The proposed geostatistical and statistical methods can improve prediction of flow behavior even if conditioning data are sparse and radar data are noisy. The derived geostatistical models of stratigraphy, facies and diagenesis are appropriate for analogous deltaic reservoirs. Furthermore, the results can guide data acquisition, improve performance prediction, and help to upscale models
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