[pt] A inversão sísmica é uma etapa essencial na modelagem e caracterização de reservatórios que permite a estimativa de propriedades da subsuperfície a partir dos dados de reflexão sísmica. os métodos convencionais usualmente possuem um alto custo computacional ou apresentam problemas relativos à não-linearidade e à forte ambiguidade do modelo de inversão sísmica. Recentemente, com a generalizaçãodo aprendizado de máquina na geofísica, novos métodos de inversão sísmica surgiram baseados nas técnicas de aprendizado profundo. Entretanto, a aplicação prática desses métodos é limitada devido a ausência de uma abordagem probabilística capaz de lidar com as incertezas inerentes ao problema da inversão sísmica e/ou a necessidade de dados de treinamento completos e representativos. Para superar essas limitações, um novo método é proposto para inverter dados de reflexão sísmica diretamente para modelos da subsuperfície de alta resolução. O método proposto explora a capacidade das redes neurais convolucionais em extrair representações significativas e complexas de dados espacialmente estruturados, combinada à simulação estocástica geoestatística. Em abordagem auto-supervisionada, modelos físicos são incorporados no sistema de inversão com o objetivo de potencializar o uso das medições indiretas e imprecisas, mas amplamente distribuídas do método sísmico. As realizações geradas com simulação geoestatística fornecem informações adicionais com maior resolução espacial do que a originalmente encontrada nos dados sísmicos. Quando utilizadas como entrada do sistema de inversão, elas permitem a geração de múltiplos modelos alternativos da subsuperfície. Em resumo, o método proposto é capaz de: (1) quantificar as incertezas das previsões, (2) modelar a relação complexa e não-linear entre os dados observados e o modelo da subsuperfície, (3) estender a largura de banda sísmica nas extremidades baixa e alta do espectro de parâmetros de frequência, e (4) diminuir a necessidade de dados de treinamento anotados. A metodologia proposta é inicialmente descrita no domínio acústico para inverter modelos de impedância acústica a partir de dados sísmicos pós-empilhados. Em seguida, a metodologia é generalizada para o domínio elástico para inverter a partir de dados sísmicos pré-empilhados modelos de velocidade da onda P, da velocidade da onda S e de densidade. Em seguida, a metodologia proposta é estendida para a inversão sísmica petrofísica em um fluxo de trabalho simultâneo. O método foi validado em um caso sintético e aplicado com sucesso a um caso tridimensional de um reservatório brasileiro real. Os modelos invertidos são comparados àqueles obtidos a partir de uma inversão sísmica geoestatística iterativa. A metodologia proposta permite obter modelos similares, mas tem a vantagem de gerar soluções alternativas em maior número, permitindo explorar de forma mais efetiva o espaço de parâmetros do modelo quando comparada à inversão sísmica geoestatística iterativa. / [en] Seismic inversion allows the prediction of subsurface properties from seismic reflection data and is a key step in reservoir modeling and characterization. Traditional seismic inversion methods usually come with a high computational cost or suffer from issues concerning the non-linearity and the strong non-uniqueness of the seismic inversion model. With the generalization of machine learning in geophysics, deep learning methods have been proposed as efficient seismic inversion methods. However, most of them lack a probabilistic approach to deal with the uncertainties inherent in the seismic inversion problems and/or rely on complete and representative training data, which is often scarcely available. To overcome these limitations, we introduce a novel seismic inversion method that explores the ability of deep convolutional neural networks to extract meaningful and complex representations from spatially structured data, combined with geostatistical stochastic simulation to efficiently invert seismicn reflection data directly for high-resolution subsurface models. Our method incorporates physics constraints, sparse direct measurements, and leverages the use of imprecise but widely distributed indirect measurements as represented by the seismic data. The geostatistical realizations provide additional information with higher spatial resolution than the original seismic data. When used as input to our inversion system, they allow the generation of multiple possible outcomes for the uncertain model. Our approach is fully unsupervised, as it does not depend on ground truth input-output pairs. In summary, the proposed method is able to: (1) provide uncertainty assessment of the predictions, (2) model the complex non-linear relationship between observed data and model, (3) extend the seismic bandwidth at both low and high ends of the frequency parameters spectrum, and (4) lessen the need for large, annotated training data. The proposed methodology is first described in the acoustic domain to invert acoustic impedance models from full-stack seismic data. Next, it is generalized for the elastic domain to invert P-wave velocity, S-wave velocity and density models from pre-stack seismic data. Finally, we show that the proposed methodology can be further extended to perform petrophysical seismic inversion in a simultaneous workflow. The method was tested on a synthetic case and successfully applied to a real three-dimensional case from a Brazilian reservoir. The inverted models are compared to those obtained from a full iterative geostatistical seismic inversion. The proposed methodology allows retrieving similar models but has the advantage of generating alternative solutions in greater numbers, providing a larger exploration of the model parameter space in less time than the geostatistical seismic inversion.

The multi-utility tunnel has received increased attention as an alternative method for the installation of subsurface infrastructure for the distribution of electricity, water, sewage and district heating. In previous research, the multi-utility tunnel (MUT) has been described as a more sustainable technology compared to the conventionally used technique where the cables and pipes are placed with open-cut excavation (OCE), especially when the entire life cycle is taken into account. This thesis aims to contribute to an improved understanding of MUT's sustainability performance in relation to conventional installation using open-cut excavation. This is done by using literature study, interview study and quantitative sustainability assessments to gain an understanding of the current state of knowledge. Furthermore, this thesis also focuses on how knowledge can be deepened with the help of quantitative sustainability assessments and the challenges of conducting this type of assessment. This thesis shows that the state of knowledge regarding MUT's sustainability performance is low and scattered, with a lack of a holistic approach. Direct economic performance has gained the most attention, followed by indirect and social impact, and the environmental impact has so far barely been assessed. The sustainability performance depends to a large extent on the conditions of the specific case, and these should be considered when assessing the technology. Quantitative assessments have the potential to help deepen the knowledge of the sustainability implications of using MUT. The characteristics of MUT have some similarities with other types of physical infrastructure. Similarities are that the systems are long-lived, have project conditions that affect sustainability performance, and impact a broad spectrum of actors. One difference to typical infrastructure systems is that the owner and management structure of MUT is, by design, more complex as several types of utility systems are in use. The characteristics of MUT give some practical considerations that need to be addressed: data availability, including practitioners; detailed data; transparency; and flexibility. This thesis highlights the complexity of assessing MUT´s sustainability performance and advocates that future studies should have a learning-oriented approach so that the knowledge level can collectively and gradually improve over time rather than focusing on decision-oriented studies. / <p><strong>Funding agencies:</strong> Kampradstiftelsen</p>

Recent advances in high-throughput sequencing (HTS) technologies have revolutionized our understanding of microbial diversity and composition in relation to their environment. HTS-based characterization of metabolically active (RNA-derived) and total (DNA-derived) fungal communities in different terrestrial habitats has revealed profound differences in both richness and community compositions. However, such DNA- and RNA-based HTS comparisons are widely missing for fungal communities of groundwater aquifers in the terrestrial biogeosphere. Therefore, in this study, we extracted DNA and RNA from groundwater samples of two pristine aquifers in the Hainich CZE and employed paired-end Illumina sequencing of the fungal nuclear ribosomal internal transcribed spacer 2 (ITS2) region to comprehensively test difference/similarities in the “total” and “active” fungal communities. We found no significant differences in the species richness between the DNA- and RNA-derived fungal communities, but the relative abundances of various fungal operational taxonomic units (OTUs) appeared to differ. We also found the same set of environmental parameters to shape the “total” and “active” fungal communities in the targeted aquifers. Furthermore, our comparison also underlined that about 30%–40% of the fungal OTUs were only detected in RNA-derived communities. This implies that the active fungal communities analyzed by HTS methods in the subsurface aquifers are actually not a subset of supposedly total fungal communities. In general, our study highlights the importance of differentiating the potential (DNA-derived) and expressed (RNA-derived) members of the fungal communities in aquatic ecosystems.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

The movement of nitrogen from the surface, where it is applied as fertilizer, to groundwater is of importance due to the health concerns associated with nitrate and potential eutrophication of groundwater impacted surface water. The computer model, PRZM (Pesticide Root Zone Model) was used to simulate the transportation of nitrogen through the soil column, past the crop root zone to groundwater. Then MOC (Method of Characterization), a groundwater model, was used to transport the nitrogen that had reached the water table offsite. Results were compared to existing field data in an attempt to verify the validity of the simulation. / Master of Science