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Ground Penetrating Radar (GPR) is a near surface geophysical method that has been used for applications including archaeological sites, groundwater contamination, and geological mapping. Though GPR has been used extensively, advancements on data processing had a great impact on data resolution. GPR is frequently used for shallow investigations because of the high resolution near the surface; however, it has limited depth of penetration and vertical bed resolution. Vertical resolution is proportional to frequency. The thickness of beds in the subsurface is conventionally resolved to one-fourth the wavelength of the central frequency. The vertical resolution at a central frequency of 200 MHz in a beach environment is approximately 17 cm; however, that value does not accurately represent fine-scale lamina and pinching out of beds, which can be an order magnitude or more than the current resolution. Complex trace analysis and spectral analysis have been used in seismic reflection for characterizing structures and stratigraphy. These "attributes" have been used to indicate hydrocarbon presence in industry. The same concept was applied to a theoretical GPR model and tested against actual data. The theoretical GPR model was created to simulate a case in which two ideal 0 degree phase Ricker wavelets merge. The wavelets constructively "add" together to create a composite wavelet with double amplitude. Applying a spectral analysis reveals that an attribute in the form of instantaneous phase and instantaneous frequency can be used to image the beds merging. The spectral analysis was applied to field data from North Padre Island National Seashore, Texas, to image "pinch-outs". Multiple survey arrays were collected using a 200 MHz frequency antenna to image internal dune structures. The results showed anomalous features at merging beds and contacts between interfaces. The results directly influence sedimentological and geomorphological interpretations of internal dune structure and can be used to better understand erosional processes in coastal sedimentary environments.

Master of Science / Department of Geology / Matthew Totten / Due to their high resolution and established success rates, 3D seismic surveys have become one of the most important tools in many hydrocarbon exploration programs. Basic interpretation of seismic reflectors alone, however, may result in inaccurate predictions of subsurface geology. Historically, seismic attributes have played a particularly important role in the characterization of the lithological and petrophysical properties of hydrocarbon reservoirs in Kansas channel fill lithofacies. Integration of an analysis based on post-stack seismic attributes may drastically reduce the chances of drilling in unsuitable locations. Previous theses have focused on establishing a suitable 3D seismic attribute analysis workflow for use in the determination of hydrocarbon production potential in areas of Ness County, Kansas, USA (Abbas, 2009; Phillip, 2011). By applying a similar workflow in the analysis of additional 3D seismic and well log data obtained from a nearby area in Ness County, and comparing those results to existing borehole and production data, this study seeks to test the hypothesis that seismic attribute analysis is a crucial component in the delineation of heterogeneous reservoir stratigraphy in Kansas lithologies. Time-structure maps, in addition to time slices of several 3D seismic attributes including amplitude attenuation, acoustic impedance, and event continuity all seem to indicate that five previously drilled dry wells within the study area were outside the boundary of a meandering, Cherokee sandstone body of potential reservoir quality. Additionally, comparisons of the results of this research to previous studies conducted in Ness County have provided an opportunity to assess, and potentially contribute to, paleodepositional interpretations made through the utilization of a similar workflow (Raef et al., in press). The results of this study seem to support a broadly NE-SW trending meandering channel system, which is in agreement with the interpretations of Raef et al., and the findings of Ramaker (2009).

Master of Science / Department of Geology / Abdelmoneam Raef / This research presents a workflow integrating several post-stack seismic attributes to assist in understanding the development history of Weirman Field, Ness County, KS. This study contributes to shaping future drilling plans by establishing a workflow combining analysis of seismic attributes and well cuttings to locate a channel fill zone of better reservoir quality, and to highlight reservoir boundaries due to compartmentalization. In this study, I have successfully outlined a fluvial channel, which is expected to be significantly different in terms of petrophysical properties. The Pennsylvanian aged Cherokee sandstones that potentially comprise channel fill lithofacies, in this study, have been linked to oil production throughout the state of Kansas. It is important to understand channel sandstones when evaluating drilling prospects, because of their potential as an oil reservoir and unpredictable shapes and locations. Since their introduction in the 1970s, seismic attributes have become an essential part of lithological and petrophysical characterization of hydrocarbon reservoirs. Seismic attributes can correlate to and help reveal certain subsurface characteristics and specific geobodies that cannot be distinguished otherwise. Extracting and analyzing acoustic impedance, root-mean-square amplitude and amplitude attenuation, guided by a time window focused on the top of the Mississippian formation, resulted in an understanding of the key seismic channel-facies framework and helped to explain some of the disappointing drilling results at Weirman Field. To form a better understanding of these seismic attributes, this study combined certain attributes and overlayed them in partially transparent states in order to summarize and better visualize the resulting data. A preliminary study of spectral decomposition, which was introduced in the late 1990s, was preformed, and a more in-depth study of this multi-resolution attribute is recommended for future study of this particular field. This study also recommends integrating the revealed compartmentalization boundary and the seismic channel-facies framework in future drilling plans of Weirman Field.

Com o crescimento do volume e dos tipos de dados, a necessidade de analisar e entender o que estes representam e como estão relacionados tem se tornado crucial. Técnicas de visualização baseadas em projeções multidimensionais ganharam espaço e interesse como uma das possíveis ferramentas de auxílio para esse problema, proporcionando um forma simples e rápida de identificar padrões, reconhecer tendências e extrair características antes não óbvias no conjunto original. No entanto, a projeção do conjunto de dados em um espaço de menor dimensão pode não ser suficiente, em alguns casos, para responder ou esclarecer certas perguntas feitas pelo usuário, tornando a análise posterior à projeção crucial para a correta interpretação da visualização observada. Logo, a interatividade, aplicada à necessidade do usuário, é uma fator essencial para análise. Neste contexto, este projeto de mestrado tem como principal objetivo criar metáforas visuais baseadas em atributos, através de medidas estatísticas e artefatos para detecção de ruídos e grupos similares, para auxiliar na exploração e análise dos dados projetados. Além disso, propõe-se disponibilizar, em navegadores Web, as técnicas de visualização de dados multidimensionais desenvolvidas pelo Grupo de Processamento Visual e Geométrico do ICMC-USP. O desenvolvimento do projeto como plataforma Web inspira-se na dificuldade de instalação e execução que certos projetos de visualização possuem, como problemas causados por diferentes versões de IDEs, compiladores e sistemas operacionais. Além disso, o fato do projeto estar disponível online para execução tem como propósito facilitar o acesso e a divulgação das técnicas propostas para o público geral. / With the growing number and types of data, the need to analyze and understand what they represent and how they are related has become crucial. Visualization techniques based on multidimensional projections have gained space and interest as one of the possible tools to aid this problem, providing a simple and quick way to identify patterns, recognize trends and extract features previously not obvious in the original set. However, the data set projection in a smaller space may not be sufficient in some cases to answer or clarify certain questions asked by the user, making the posterior projection analysis crucial for the exploration and understanding of the data. Thus, interactivity in the visualization, applied to the users needs, is an essential factor for analysis. In this context, this master projects main objective consists to create visual metaphors based on attributes, through statistical measures and artifacts for detecting noise and similar groups, to assist the exploration and analysis of projected data. In addition, it is proposed to make available, in Web browsers, the multidimensional data visualization techniques developed by the Group of Visual and Geometric Processing at ICMC-USP. The development of the project as a Web platform was inspired by the difficulty of installation and running that certain visualization projects have, mainly due different versions of IDEs, compilers and operating systems. In addition, the fact that the project is available online for execution aims to facilitate the access and dissemination of technical proposals for the general public.

Upper to lower delta plain fluvial sand bodies, sealed by delta plain mudstones, form important hydrocarbon reservoir targets. Modelling complex geobodies in the subsurface is challenging, with a significant degree of uncertainty on dimensions, distribution and connectivity. Studies of modern and ancient paralic systems have produced a myriad of nomenclature and hierarchy schemes for classifying fluvial architectural elements; often lacking clearly-defined terminology. These are largely based on outcrop data where lateral and vertical relationships of bounding scour surfaces can be assessed in detail. Many of these key defining criteria are difficult to recognise or cannot be obtained from typical 3D seismic reflection data at reservoir depths greater than or equal to 2 km subsurface. This research provides a detailed statistical analysis of the Triassic fluvio-deltaic Mungaroo Formation on the North West Shelf of Australia, which is one of the most important gas plays in the world. A multidisciplinary approach addresses the challenge of characterising the reservoir by utilising an integrated dataset of 830 m of conventional core, wireline logs from 21 wells (penetrating up to 1.4 km of the upper Mungaroo Fm) and a 3D seismic volume covering approximately 10,000 km2. Using seismic attribute analysis and frequency decomposition, constrained by well and core data, the planform geobody geometries and dimensions of a variety of architectural elements at different scales of observation are extracted. The results produce a statistically significant geobody database comprising over 27,000 measurements made from more than 6,000 sample points. Three classes of geobodies are identified and interpreted to represent fluvial channel belts and channel belt complexes of varying scales. Fluvial geobody dimensions and geomorphology vary spatially and temporally and the inferred controls on reservoir distribution and architecture are discussed. Results document periods of regression and transgression, interpreted in relation to potential allocyclic and autocyclic controls on the evolution of the depositional system. Statistical analysis of width-to-thickness dimensions and key metrics, such as sinuosity, provided a well-constrained and valuable dataset that augments, and has been compared to, existing published datasets. Uncertainty in interpretation caused by data resolution is addressed; something recognised in many other studies of paralic systems. Given the data distribution, type and resolution, geobodies have possible interpretations as either incised valleys or amalgamated channel belts, with implications for developing predictive models of the system. This study offers the first published, statistically significant dataset for the Mungaroo Formation. It builds upon previous regional work, offering a detailed analysis of this continental scale paralic system and provides insight into the controls and mechanisms that influenced its spatial and temporal evolution. Focusing on improved understanding of geobody distribution and origin, the statistical parameters generated provide a robust dataset that can be used for 3D static reservoir models of analogue systems. Thus, helping to constrain potential geobody dimensions and reduce the uncertainties associated with modelling.

Based on technological advances made within the past decades, ground-penetrating radar (GPR) has become a well-established, non-destructive subsurface imaging technique. Catalyzed by recent demands for high-resolution, near-surface imaging (e.g., the detection of unexploded ordnances and subsurface utilities, or hydrological investigations), the quality of today's GPR-based, near-surface images has significantly matured. At the same time, the analysis of oil and gas related reflection seismic data sets has experienced significant advances. Considering the sensitivity of attribute analysis with respect to data positioning in general, and multi-trace attributes in particular, trace positioning accuracy is of major importance for the success of attribute-based analysis flows. Therefore, to study the feasibility of GPR-based attribute analyses, I first developed and evaluated a real-time GPR surveying setup based on a modern tracking total station (TTS). The combination of current GPR systems capability of fusing global positioning system (GPS) and geophysical data in real-time, the ability of modern TTS systems to generate a GPS-like positional output and wireless data transmission using radio modems results in a flexible and robust surveying setup. To elaborate the feasibility of this setup, I studied the major limitations of such an approach: system cross-talk and data delays known as latencies. Experimental studies have shown that when a minimal distance of ~5 m between the GPR and the TTS system is considered, the signal-to-noise ratio of the acquired GPR data using radio communication equals the one without radio communication. To address the limitations imposed by system latencies, inherent to all real-time data fusion approaches, I developed a novel correction (calibration) strategy to assess the gross system latency and to correct for it. This resulted in the centimeter trace accuracy required by high-frequency and/or three-dimensional (3D) GPR surveys. Having introduced this flexible high-precision surveying setup, I successfully demonstrated the application of attribute-based processing to GPR specific problems, which may differ significantly from the geological ones typically addressed by the oil and gas industry using seismic data. In this thesis, I concentrated on archaeological and subsurface utility problems, as they represent typical near-surface geophysical targets. Enhancing 3D archaeological GPR data sets using a dip-steered filtering approach, followed by calculation of coherency and similarity, allowed me to conduct subsurface interpretations far beyond those obtained by classical time-slice analyses. I could show that the incorporation of additional data sets (magnetic and topographic) and attributes derived from these data sets can further improve the interpretation. In a case study, such an approach revealed the complementary nature of the individual data sets and, for example, allowed conclusions about the source location of magnetic anomalies by concurrently analyzing GPR time/depth slices to be made. In addition to archaeological targets, subsurface utility detection and characterization is a steadily growing field of application for GPR. I developed a novel attribute called depolarization. Incorporation of geometrical and physical feature characteristics into the depolarization attribute allowed me to display the observed polarization phenomena efficiently. Geometrical enhancement makes use of an improved symmetry extraction algorithm based on Laplacian high-boosting, followed by a phase-based symmetry calculation using a two-dimensional (2D) log-Gabor filterbank decomposition of the data volume. To extract the physical information from the dual-component data set, I employed a sliding-window principle component analysis. The combination of the geometrically derived feature angle and the physically derived polarization angle allowed me to enhance the polarization characteristics of subsurface features. Ground-truth information obtained by excavations confirmed this interpretation. In the future, inclusion of cross-polarized antennae configurations into the processing scheme may further improve the quality of the depolarization attribute. In addition to polarization phenomena, the time-dependent frequency evolution of GPR signals might hold further information on the subsurface architecture and/or material properties. High-resolution, sparsity promoting decomposition approaches have recently had a significant impact on the image and signal processing community. In this thesis, I introduced a modified tree-based matching pursuit approach. Based on different synthetic examples, I showed that the modified tree-based pursuit approach clearly outperforms other commonly used time-frequency decomposition approaches with respect to both time and frequency resolutions. Apart from the investigation of tuning effects in GPR data, I also demonstrated the potential of high-resolution sparse decompositions for advanced data processing. Frequency modulation of individual atoms themselves allows to efficiently correct frequency attenuation effects and improve resolution based on shifting the average frequency level. GPR-based attribute analysis is still in its infancy. Considering the growing widespread realization of 3D GPR studies there will certainly be an increasing demand towards improved subsurface interpretations in the future. Similar to the assessment of quantitative reservoir properties through the combination of 3D seismic attribute volumes with sparse well-log information, parameter estimation in a combined manner represents another step in emphasizing the potential of attribute-driven GPR data analyses. / Geophysikalische Erkundungsmethoden haben in den vergangenen Jahrzehnten eine weite Verbreitung bei der zerstörungsfreien beziehungsweise zerstörungsarmen Erkundung des oberflächennahen Untergrundes gefunden. Im Vergleich zur Vielzahl anderer existierender Verfahrenstypen ermöglicht das Georadar (auch als Ground Penetrating Radar bezeichnet) unter günstigen Standortbedingungen Untersuchungen mit der höchsten räumlichen Auflösung. Georadar zählt zu den elektromagnetischen (EM) Verfahren und beruht als Wellenverfahren auf der Ausbreitung von hochfrequenten EM-Wellen, das heisst deren Reflektion, Refraktion und Transmission im Untergrund. Während zweidimensionale Messstrategien bereits weit verbreitet sind, steigt gegenwärtig das Interesse an hochauflösenden, flächenhaften Messstrategien, die es erlauben, Untergrundstrukturen dreidimensional abzubilden. Ein dem Georadar prinzipiell ähnliches Verfahren ist die Reflexionsseismik, deren Hauptanwendung in der Lagerstättenerkundung liegt. Im Laufe des letzten Jahrzehnts führte der zunehmende Bedarf an neuen Öl- und Gaslagerstätten sowie die Notwendigkeit zur optimalen Nutzung existierender Reservoirs zu einer verstärkten Anwendung und Entwicklung sogenannter seismischer Attribute. Attribute repräsentieren ein Datenmaß, welches zu einer verbesserten visuellen Darstellung oder Quantifizierung von Dateneigenschaften führt die von Relevanz für die jeweilige Fragestellung sind. Trotz des Erfolgs von Attributanalysen bei reservoirbezogenen Anwendungen und der grundlegenden Ähnlichkeit von reflexionsseismischen und durch Georadar erhobenen Datensätzen haben attributbasierte Ansätze bisher nur eine geringe Verbreitung in der Georadargemeinschaft gefunden. Das Ziel dieser Arbeit ist es, das Potential von Attributanalysen zur verbesserten Interpretation von Georadardaten zu untersuchen. Dabei liegt der Schwerpunkt auf Anwendungen aus der Archäologie und dem Ingenieurwesen. Der Erfolg von Attributen im Allgemeinen und von solchen mit Berücksichtigung von Nachbarschaftsbeziehungen im Speziellen steht in engem Zusammenhang mit der Genauigkeit, mit welcher die gemessenen Daten räumlich lokalisiert werden können. Vor der eigentlichen Attributuntersuchung wurden deshalb die Möglichkeiten zur kinematischen Positionierung in Echtzeit beim Georadarverfahren untersucht. Ich konnte zeigen, dass die Kombination von modernen selbstverfolgenden Totalstationen mit Georadarinstrumenten unter Verwendung von leistungsfähigen Funkmodems eine zentimetergenaue Positionierung ermöglicht. Experimentelle Studien haben gezeigt, dass die beiden potentiell limitierenden Faktoren - systeminduzierte Signalstöreffekte und Datenverzögerung (sogenannte Latenzzeiten) - vernachlässigt beziehungsweise korrigiert werden können. In der Archäologie ist die Untersuchung oberflächennaher Strukturen und deren räumlicher Gestalt wichtig zur Optimierung geplanter Grabungen. Das Georadar hat sich hierbei zu einem der wohl am meisten genutzten zerstörungsfreien geophysikalischen Verfahren entwickelt. Archäologische Georadardatensätze zeichnen sich jedoch oft durch eine hohe Komplexität aus, was mit der wiederholten anthropogenen Nutzung des oberflächennahen Untergrundes in Verbindung gebracht werden kann. In dieser Arbeit konnte gezeigt werden, dass die Verwendung zweier unterschiedlicher Attribute zur Beschreibung der Variabilität zwischen benachbarten Datenspuren eine deutlich verbesserte Interpretation in Bezug auf die Fragestellung ermöglicht. Des Weiteren konnte ich zeigen, dass eine integrative Auswertung von mehreren Datensätzen (methodisch sowie bearbeitungstechnisch) zu einer fundierteren Interpretation führen kann, zum Beispiel bei komplementären Informationen der Datensätze. Im Ingenieurwesen stellen Beschädigungen oder Zerstörungen von Versorgungsleitungen im Untergrund eine große finanzielle Schadensquelle dar. Polarisationseffekte, das heisst Änderungen der Signalamplitude in Abhängigkeit von Akquisitions- sowie physikalischen Parametern stellen ein bekanntes Phänomen dar, welches in der Anwendung bisher jedoch kaum genutzt wird. In dieser Arbeit wurde gezeigt, wie Polarisationseffekte zu einer verbesserten Interpretation verwendet werden können. Die Überführung von geometrischen und physikalischen Attributen in ein neues, so genanntes Depolarisationsattribut hat gezeigt, wie unterschiedliche Leitungstypen extrahiert und anhand ihrer Polarisationscharakteristika klassifiziert werden können. Weitere wichtige physikalische Charakteristika des Georadarwellenfeldes können mit dem Matching Pursuit-Verfahren untersucht werden. Dieses Verfahren hatte in den letzten Jahren einen großen Einfluss auf moderne Signal- und Bildverarbeitungsansätze. Matching Pursuit wurde in der Geophysik bis jetzt hauptsächlich zur hochauflösenden Zeit-Frequenzanalyse verwendet. Anhand eines modifizierten Tree-based Matching Pursuit Algorithmus habe ich demonstriert, welche weiterführenden Möglichkeiten solche Datenzerlegungen für die Bearbeitung und Interpretation von Georadardaten eröffnen. Insgesamt zeigt diese Arbeit, wie moderne Vermessungstechniken und attributbasierte Analysestrategien genutzt werden können um dreidimensionale Daten effektiv und genau zu akquirieren beziehungsweise die resultierenden Datensätze effizient und verlässlich zu interpretieren.

Com o crescimento do volume e dos tipos de dados, a necessidade de analisar e entender o que estes representam e como estão relacionados tem se tornado crucial. Técnicas de visualização baseadas em projeções multidimensionais ganharam espaço e interesse como uma das possíveis ferramentas de auxílio para esse problema, proporcionando um forma simples e rápida de identificar padrões, reconhecer tendências e extrair características antes não óbvias no conjunto original. No entanto, a projeção do conjunto de dados em um espaço de menor dimensão pode não ser suficiente, em alguns casos, para responder ou esclarecer certas perguntas feitas pelo usuário, tornando a análise posterior à projeção crucial para a correta interpretação da visualização observada. Logo, a interatividade, aplicada à necessidade do usuário, é uma fator essencial para análise. Neste contexto, este projeto de mestrado tem como principal objetivo criar metáforas visuais baseadas em atributos, através de medidas estatísticas e artefatos para detecção de ruídos e grupos similares, para auxiliar na exploração e análise dos dados projetados. Além disso, propõe-se disponibilizar, em navegadores Web, as técnicas de visualização de dados multidimensionais desenvolvidas pelo Grupo de Processamento Visual e Geométrico do ICMC-USP. O desenvolvimento do projeto como plataforma Web inspira-se na dificuldade de instalação e execução que certos projetos de visualização possuem, como problemas causados por diferentes versões de IDEs, compiladores e sistemas operacionais. Além disso, o fato do projeto estar disponível online para execução tem como propósito facilitar o acesso e a divulgação das técnicas propostas para o público geral. / With the growing number and types of data, the need to analyze and understand what they represent and how they are related has become crucial. Visualization techniques based on multidimensional projections have gained space and interest as one of the possible tools to aid this problem, providing a simple and quick way to identify patterns, recognize trends and extract features previously not obvious in the original set. However, the data set projection in a smaller space may not be sufficient in some cases to answer or clarify certain questions asked by the user, making the posterior projection analysis crucial for the exploration and understanding of the data. Thus, interactivity in the visualization, applied to the users needs, is an essential factor for analysis. In this context, this master projects main objective consists to create visual metaphors based on attributes, through statistical measures and artifacts for detecting noise and similar groups, to assist the exploration and analysis of projected data. In addition, it is proposed to make available, in Web browsers, the multidimensional data visualization techniques developed by the Group of Visual and Geometric Processing at ICMC-USP. The development of the project as a Web platform was inspired by the difficulty of installation and running that certain visualization projects have, mainly due different versions of IDEs, compilers and operating systems. In addition, the fact that the project is available online for execution aims to facilitate the access and dissemination of technical proposals for the general public.

<p>Online reviews are frequently utilized to determine a product's quality before purchase along with the photographs and one-to-five star ratings. The research addressed the two distinct problems observed in the review systems. </p> <p>First, due to thousands of reviews for a product, the different characteristics of customer evaluations, such as consumer sentiments, cannot be understood by manually reading only a few reviews. Second, from these reviews, it is extremely hard to understand the change in these sentiments and other important product aspects over the years (temporal analysis). To address these problems, the study focused on 2 main research parts.</p> <p>Part one of the research was focused on answering how topic modelling and sentiment analysis can work together to give deeper understanding on attribute-based product review. The second part compared different topic modelling approaches to evaluate the performances and advantages of emerging NLP models. For this purpose, a dataset consisting of 469 publicly accessible Amazon evaluations of the Kindle E-reader and 15,000 reviews of iPhone products was utilized to examine sentiment Analysis and Topic modelling. Latent Dirichlet Allocation topic model and BERTopic topic model were used to perform topic modelling and to acquire the diverse topics of concern. Sentiment Analysis was carried out to better understand each topic's positive and negative tones. Topic analysis of Kindle user evaluations revealed the following major themes: (a) leisure consumption, (b) utility as a gift, (c) pricing, (d) parental control, (e) reliability and durability, and (f) charging. While the main themes emerged from the analysis of iPhone reviews depended on the model and year of the device, some themes were found to be consistent across all the iPhone models including (a) Apple vs Android (b) utility as gift and (c) service. The study's approach helped to analyze customer reviews for any product, and the study results provided a deeper understanding of the product's strengths and weaknesses based on a comprehensive analysis of user feedback useful for product makers, retailers, e-commerce platforms, and consumers.</p>

abstract: Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights. / Dissertation/Thesis / Doctoral Dissertation Civil and Environmental Engineering 2015