<p>Investigating the presence of microbial communities in the Earth's deep terrestrial subsurface and the metabolic processes taking place in these environments provides insight into the some of the ultimate limits for life on Earth, as well as the potential for microbial life to exist within the subsurface of other planetary bodies. This Master's thesis project utilized phospholipid fatty acid (PLFA) analysis, in combination with carbon isotope analyses (δ¹³C and Δ¹⁴C), to explore the presence and activity of microbial communities living within deep terrestrial subsurface fracture water systems and low permeability, deep sedimentary rocks. Deep fracture water systems, ranging from 0.9 to 3.2 km below land surface, were sampled for microbial communities via deep mine boreholes in the Witwatersrand Basin of South Africa. PLFA concentrations revealed low biomass microbial communities, ranging from 2x10¹ to 5x10⁴ cells per mL and the PLFA profiles contained indicators for environmental stressors, including high temperatures and nutrient deprivation. δ¹³C and Δ¹⁴C analyses of PLFAs and potential carbon sources (dissolved inorganic carbon (DIC), dissolved organic carbon (DOC) and methane) identified microbial utilization of methane in some systems and utilization of DIC in others. Evidence for microbial oxidation of methane and chemoautotrophy in these systems is consistent with a self-sustaining deep terrestrial subsurface biosphere that is capable of surviving independent of the photosphere. Viable microbial communities were also identified within deep (334 to 694 m depth) sedimentary rock cores sampled from the Michigan Basin, Canada. PLFA analyses revealed microbial cell densities ranging from 1-3 x 10⁵ cells/mL and identified PLFA indicators for environmental stressors. These results demonstrate the ubiquity of microbial life in the deep terrestrial subsurface and provide insight into microbial carbon sources and cycling in deep microbial systems which may persist in isolation over geologic timescales.</p> / Master of Science (MSc)

Soil-bentonite cutoff walls are commonly used to contain contaminants in the subsurface. A key property in determining the effectiveness of a cutoff wall is its hydraulic conductivity. There are important difficulties and uncertainties regarding the accuracy of commonly used methods of measuring the hydraulic conductivity of cutoff walls. When predicting contaminant transport through cutoff walls, common practice is to use the average hydraulic conductivity of the wall. There are some cases, however, such as circumferential cutoff walls with inward hydraulic gradients, where it is also important to consider the variability in hydraulic conductivity from point to point in the wall in contaminant transport studies. A pilot-scale facility was envisioned where subsurface barrier issues such as those mentioned above could be studied. In 1998, the Subsurface Barrier Test Facility (SBTF) was constructed. In this facility, pilot-scale subsurface barriers can be installed using real construction equipment and tested in a controlled environment. The effectiveness of various methods of measuring the hydraulic conductivity of cutoff walls was studied by building and testing three pilot-scale soil-bentonite cutoff walls at the SBTF. The following currently used test methods were evaluated: API tests on grab samples, lab tests on undisturbed samples, piezometer tests (slug tests), and piezocone soundings. The use of slug tests in cutoff walls was improved in this research in the areas of avoiding hydraulic fracture and accounting for the close proximity of the trench walls. The SBTF allows for measurement of the global, average hydraulic conductivity of an installed pilot-scale cutoff wall, which is a useful value to compare to the results of the above-mentioned tests. The two main factors differentiating the results of the different test methods used for the pilot-scale walls were remolding and sample size. Remolding of the API samples significantly reduced the hydraulic conductivity of these samples compared to the hydraulic conductivity measured in lab tests on undisturbed samples, which were of similar size. For the other tests, the degree and extent of remolding were less significant compared to in the API tests. For these tests, the scale of the measurement is believed to be the main factor differentiating the results. Hydraulic conductivity was found to increase as the sample volume increased, with the global measurement of the average hydraulic conductivity producing the highest value. The influence of variability in hydraulic conductivity on contaminant transport through cutoff walls was studied from a theoretical standpoint using the one-dimensional advection-diffusion equation. Charts were developed that can be used to estimate the flux through a cutoff wall based on knowledge of the average hydraulic conductivity of the wall and an estimate of the variability in hydraulic conductivity. Data sets of hydraulic conductivity from lab tests on soil-bentonite samples from four cutoff wall case histories were used to estimate typical values of variability. The contaminant transport analyses showed that the effect of variability may be significant when the hydraulic gradient opposes the concentration gradient, which is the case for a circumferential cutoff wall with an inward hydraulic gradient. The goal of a circumferential cutoff wall with an inward hydraulic gradient is to reduce the outward diffusive flux of contaminant by inducing an inward advective flux. The effect of variability in hydraulic conductivity is to reduce the effectiveness of this scheme. / Ph. D.

A gram negative microaerophilic bacterium, designated Pseudomonas sp. strain MR 100, was isolated from a depth of 463 meters at the Savannah River DOE site and identified using 16S rDNA sequencing and DNA-DNA reassociation. Micro aerophiles from the Middendorf formation were isolated by use of a semi-solid agar assay, and constituted 10% of the plateable microorganisms. Genetic identification involved the isolation of genomic DNA and amplification of the gene encoding 16S rRNA by PCR, using universal primers. The amplified DNA was sequenced and compared to 16S rRNA sequences in Genbank. High sequence similarity (98.5%) was observed with the <i>Pseudomonas mendocina</i> type strain, indicating a similarity to the (Group I) pseudomonads. DNA-DNA reassociation was performed between <i>Pseudomonas</i> sp. strain MR 100 and 11 representative p seudomonads using the S 1 nuclease method. Strain MR 100 was found to be 20% homologous to the <i>Pseudomonas mendocina</i> type strain, 10% homologous to <i>Pseudomonas alcaligenes</i>, and 5% homologous to <i>Pseudomonas aeruginosa</i>. Data from biochemical tests confirm the hypothesis that strain MR 100 is a novel species of <i>Pseudomonas</I. It was able to accumulate poly-β-hydroxybutyrate intracellularly, while it lacked the ability to produce cellular pigments, which is unique among the (Group I) pseudomonads. Growth occurred at oxygen concentrations of 20/0 and 21%, with similar growth rates and final cell densities. / Master of Science

The increased use of alcohols as gasoline additives, and possible substitutes, has prompted the investigation of the fate of gasoline/alcohol mixtures in the environment. In situ bioremediation is one technique that can successfully be applied to remove ground water contaminants particularly in situations where the adsorptive capacity of the soil plays a major role. Frequently, enhanced in situ bioremediation techniques rely on indigenous microorganisms to degrade ground water contaminants; this technique may sometimes include the addition of acclimated bacteria. In this study, soil microcosms were constructed in order to simulate the conditions found in a saturated aerobic aquifer. The biodegradation potential of methanol, benzene, and m-xylene was investigated. Uncontaminated soil from the surface, 12, 16.5, and 18 foot depths was utilized to observe the differences in microbial responses throughout the soil profile. The biodegradation potential of the indigenous microbiota was determined and compared to that of benzene acclimated bacteria, for all the compounds in the mixture. To observe the impact that chemical and physical soil characteristics may have on microbial responses, soils from each depth were classified on the basis of their particle size, moisture content and pH. Substantial methanol, benzene, and m-xylene biodegradation by the indigenous microorganisms occurred in all subsurface soils. While methanol was readily biodegradable over concentrations ranging from about 80 mg/L to about 200 mg/L, benzene inhibited methanol biodegradation at about 125 mg/L in all soil depths. The addition of benzene acclimated bacteria considerably increased the biodegradation rates of all compounds in the mixture. Such increases in biodegradation rates may be attributed to the activities of both groups, the indigenous microorganisms and the benzene acclimated bacteria. The results obtained by this study suggest that biodegradation of methanol, benzene, and m-xylene can readily occur in a saturated aerobic subsurface environment. The physical and chemical properties of a ground water aquifer seem to have a marked effect on microbial responses, and consequently on the biodegradation potential of water contaminants. / Master of Science

An unsupervised fuzzy logic navigation algorithm is designed and implemented in simulation for the Subsurface Explorer planetary exploration robot. The robot is intended for the subterranean exploration of Mars, and will be equipped with acoustic sensing for detecting obstacles. Measurements of obstacle distance and direction are anticipated to be imprecise however, since the performance of acoustic sensors is degraded in underground environments. Fuzzy logic is a satisfactory means of addressing imprecision in plant characteristics, and has been implemented in a variety of autonomous vehicle navigation applications. However, most fuzzy logic algorithms that perform well in unknown environments have large rule-bases or use complex methods for tuning fuzzy membership functions and rules. These qualities make them too computationally intensive to be used for planetary exploration robots like the SSX. In this thesis, we introduce an unsupervised fuzzy logic algorithm that can determine a trajectory for the SSX through unknown environments. This algorithm uses a combination of simple fusion of robot behaviors and self-tuning membership functions to determine robot navigation without resorting to the degree of complexity of previous fuzzy logic algorithms. Finally, we present some simulation results that demonstrate the practicality of our algorithm in navigating in different environments. The simulations justify the use of our fuzzy logic technique, and suggest future areas of research for fuzzy logic navigation algorithms. / Master of Science

In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contributions of this thesis. First, we present a thorough analysis of using state-of-the-art machine learning models to predict a subsurface geothermal parameter, temperature-at-depth, using a rich geo-spatial dataset across the Appalachian Basin. Specifically, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree-based models (e.g., XGBoost and Random Forest). We found that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. We also ran our model on a fine spatial grid to provide 2D continuous temperature maps at three different depths using the XGBoost model, which can be used to locate prospective geothermally active regions. Second, we develop a physics-guided machine learning model for predicting subsurface temperatures that not only uses surface temperature, thermal conductivity coefficient, and depth as input parameters, but also the heat-flux parameter that is known to be a potent indicator of temperature-at-depth values according to physics knowledge of geothermal energy problems. Since, there is no independent easy-to-use method for observing heat-flux directly or inferring it from other observed variables. We develop an innovative approach to take into account heat-flux parameters through a physics-guided clustering-regression model. Specifically, the bottom-hole temperature data is initially clustered into multiple groups based on the heat-flux parameter using Gaussian mixture model (GMM). This is followed by training neural network regression models using the data within each constant heat-flux region. Finally, a KNN classifier is trained for cluster membership prediction. Our preliminary results indicate that our proposed approach results in lower errors as the number of clusters increases because the heat-flux parameter is indirectly accounted for in the machine learning model. / Master of Science / Machine learning and artificial intelligence have transformed many research fields and industries. In this thesis, we investigate the applicability of machine learning and data-driven approaches in the field of geothermal energy exploration. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models, we show that machine learning can provide viable alternative solutions for geothermal energy mapping. First, we explore a suite of machine learning algorithms such as neural networks (DNN), Ridge regression (R-reg) models, and decision-tree based models (e.g., XGBoost and Random Forest). We find that XGBoost and Random Forests result in the highest accuracy for subsurface temperature prediction. Accuracy measures show that machine learning models are at par with physics-based models and can even outperform the thermal conductivity model. Second, we incorporate the thermal conductivity theory with machine learning and propose an innovative clustering-regression approach in the emerging area of physics-guided machine learning that results in a smaller error than black-box machine learning methods.

Nonpoint source (NPS) pollution is possibly the greatest form of contamination to our nation's waters. Nutrient pollutants, such as nitrate and ammonium, often enter aquatic ecosystems through surface and subsurface hydrological transport that drain agricultural watersheds. The over-abundance of nitrogen within these watersheds is easily transported to receiving stream and rivers, and result in aquatic ecosystem degradation. In response to the problem of nutrient loading to aquatic ecosystems, ecosystems scientists and federal and state governments have recommended the use of streamside management zones (SMZ) to reduce the amount of NPS pollutants. A small agricultural watershed in southwestern North Carolina was utilized to quantify subsurface transport of nitrate and ammonium to a naturally developing riparian area along Cartoogechaye Creek. Vertical and lateral transport of nitrate and ammonium were measured along three transect perpendicular to the stream. Transects were instrumented with time domain reflectometry (TDR) and porous cup tension lysimeters to monitor soil water and nutrient flux through the pasture and riparian area located at the base of the watershed. The HYDRUS 2-D flow and transport model was used to predict and simulate subsurface flow. Predicted flow was coupled with observed field nutrient data to quantify nutrient flux as a function of slope location. HYDRUS 2-D was capable of simulating subsurface flow (saturated and unsaturated) as a function of observed soil physical properties (bulk density, saturated hydraulic conductivity, particle size distribution, water retention characteristics) and climatic data (precipitation, air temperature, wind speed, etc.). The riparian area was effective in reducing the amount of nonpoint source pollution to a naturally developing riparian area from an agricultural watershed. Dramatic decreases in both NO3- -N and NH4+ -N in upland pasture water were observed within the riparian area. Seasonal percent reductions of NO3- from the pasture to riparian area in subsurface water within the study watershed are as follows: summer (2002) = 456%; fall (2002) = 116%; winter (2003) = 29%; spring = 9%, pasture and riparian, respectively. / Master of Science

Not many studies are currently devoted to the estimation of soil moisture from space-borne SAR data at the field scale. Superficial soil moisture is indeed generally estimated from SAR images at lower resolutions, rarely reaching the sub-kilometric scale. This is mainly due to the lack of in situ data, such as measured soil moisture and parameters indicative of the soil roughness and vegetation conditions. Moreover, when working at the kilometric scale, some hypothesis assumed while modelling the backscattered SAR signal over a vegetated area are more likely satisfied, whereas when working at higher resolutions such as the field scale, other interactions should be taken into account. Indeed, over a vegetated area the total backscattered SAR signal is usually modelled as the incoherent sum of the vegetation and the soil components, and only in the last years has been added a further contribution provoked from the presence of subsurface scatterers. In the present thesis, the just mentioned contributions are considered and modelled at the field scale for soil moisture estimation purposes. A long term Change Detection method is applied to copolarized Sentinel-1 data, with a focus on taking into account the component of the total backscattering coefficient due to the presence of subsurface scatterers, recently proposed in literature. By exploiting the strong relationships detected over the study area between the copolarized signal and the observed soil moisture, the inversion algorithm for soil moisture retrieval is adapted for considering the cases of dominant subsurface scattering mechanism. Moreover, the proper time scale of detection of subsurface scattering is identified at the field scale, providing helpful information for correcting retrieval algorithms based on SAR data also at lower spatial scales.

Yes / The process of capturing and storing carbon dioxide (CCS) was previously considered a crucial and time-sensitive approach for diminishing CO2 emissions originating from coal, oil, and gas sectors. Its implementation was seen necessary to address the detrimental effects of CO2 on the atmosphere and the ecosystem. This recognition was achieved by previous substantial study efforts. The carbon capture and storage (CCS) cycle concludes with the final stage of CO2 storage. This stage involves primarily the adsorption of CO2 in the ocean and the injection of CO2 into subsurface reservoir formations. Additionally, the process of CO2 reactivity with minerals in the reservoir formations leads to the formation of limestone through injectivities. Carbon capture and storage (CCS) is the final phase in the CCS cycle, mostly achieved by the use of marine and underground geological sequestration methods, along with mineral carbonation techniques. The introduction of supercritical CO2 into geological formations has the potential to alter the prevailing physical and chemical characteristics of the subsurface environment. This process can lead to modifications in the pore fluid pressure, temperature conditions, chemical reactivity, and stress distribution within the reservoir rock. The objective of this study is to enhance our existing understanding of CO2 injection and storage systems, with a specific focus on CO2 storage techniques and the associated issues faced during their implementation. Additionally, this research examines strategies for mitigating important uncertainties in carbon capture and storage (CCS) practises. Carbon capture and storage (CCS) facilities can be considered as integrated systems. However, in scientific research, these storage systems are often divided based on the physical and spatial scales relevant to the investigations. Utilising the chosen system as a boundary condition is a highly effective method for segregating the physics in a diverse range of physical applications. Regrettably, the used separation technique fails to effectively depict the behaviour of the broader significant system in the context of water and gas movement within porous media. The limited efficacy of the technique in capturing the behaviour of the broader relevant system can be attributed to the intricate nature of geological subsurface systems. As a result, various carbon capture and storage (CCS) technologies have emerged, each with distinct applications, associated prices, and social and environmental implications. The results of this study have the potential to enhance comprehension regarding the selection of an appropriate carbon capture and storage (CCS) application method. Moreover, these findings can contribute to the optimisation of greenhouse gas emissions and their associated environmental consequences. By promoting process sustainability, this research can address critical challenges related to global climate change, which are currently of utmost importance to humanity. Despite the significant advancements in this technology over the past decade, various concerns and ambiguities have been highlighted. Considerable emphasis was placed on the fundamental discoveries made in practical programmes related to the storage of CO2 thus far. The study has provided evidence that despite the extensive research and implementation of several CCS technologies thus far, the process of selecting an appropriate and widely accepted CCS technology remains challenging due to considerations related to its technological feasibility, economic viability, and societal and environmental acceptance.

Subsurface volume loss takes place in many geotechnical situations, and it is inherently accompanied by complex stress and displacement fields that may influence the performance of engineered geosystems. This research is a deformation-centered analysis, it depends on soil compressibility and it is implemented using finite elements. Soil stiffness plays a central role in predicting ground deformation. First, an enhanced Terzaghi’s soil compressibility model is proposed to satisfy asymptotic conditions at low and high stress levels with a small number of physically meaningful parameters. Then, the difference between small and large strain stiffness is explored using published small and large-strain stress-strain data. Typically, emphasis is placed on the laboratory-measured stiffness or compressibility; however, there are pronounced differences between laboratory measurements and field values, in part due to seating effects that prevail in small-thickness oedometer specimens. Many geosystems are subjected to repetitive loads; volumetric strains induced by drained repetitive ko-loads are experimentally investigated to identify shakedown and associated terminal density. The finite element numerical simulation environment is used to explore the effect of localized subsurface mass loss on free-surface deformation and shallow foundations settlement and bearing capacity. A stress relaxation module is developed to reproduce the change in stress associated to dissolution features and soft zone formation. The comprehensive parametric study is summarized in terms of dimensionless ratios that can be readily used for engineering applications. Field settlement data gathered at the Savannah River Site SRS are back-analyzed to compare measured values with predictions based on in situ shear wave velocity and strain-dependent stiffness reduction. The calibrated model is used to estimate additional settlements due to the pre-existing cavities, new cavities, and potential seismic events during the design life of the facility.