RSS 1.02019 - Mines Theses & Dissertations
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Proterozoic history of the southern half of the Mount Evans 7.5-minute quadrangle: evidence for a CA. 1.4 Ga orogenic event in the central Front Range, Colorado, TheThe Proterozoic history of the southeastern margin of Laurentia, especially during the Mesoproterozoic, is not well defined. Several tectonic events occurred during this time. The first two events were the Paleoproterozoic Yavapai (~1.71-1.68 Ga) and Mazatzal (~1.65-1.60 Ga) orogenies. The third is the more recently recognized Mesoproterozoic Picuris orogeny (~1.4 Ga). Evidence for the Picuris orogeny has been found in northern New Mexico, Arizona, and Colorado; however, the extent of the orogen is unclear. In Colorado, previously recognized effects of the Picuris orogeny are primarily reactivations along shear zones. The purpose of this study was to investigate the Proterozoic deformation history in the southern half of the Mt. Evans 7.5-minute quadrangle, in order to test whether pervasive folding is a result of the Mesoproterozoic Picuris orogeny and/or of earlier Paleoproterozoic orogenies. The area was selected, because of the exposure of Proterozoic ductile structures away from localized shear zones and from younger overprinting structures. Field mapping revealed evidence for four deformation events. The first (D1) consists of isoclinal folds. These are overprinted by D2 isoclinal to open folds with northerly plunging fold hinge lines. Poles to F2 axial planes plot along a great circle suggesting a third generation of folds (D3) plunging to the NNE. D4 includes non-pervasive open upright E-trending folds. These folds are only located towards the very north and south of the mapping area. Detrital zircon from one quartzite was selected for U-Pb laser ablation inductively coupled mass spectrometry (LA-ICP-MS) analysis, in order to test whether some of the metasedimentary rocks may be younger than their interpreted Paleoproterozoic age, and perhaps correlative with Mesoproterozoic metasedimentary rocks in northern New Mexico and Arizona that are associated with the Picuris orogeny. The zircon yielded ~1.55 Ga (n=4) and ~1.44 Ga (n=26) age populations and a spread of ages between ~1.81 Ga and ~1.61 Ga. The ~1.55 Ga age population may represent a true population as recognized in Defiance, Arizona the Yankee Joe and Blackjack Formations in Arizona, the Four Peaks area in Arizona, and the Tusas and Picuris Mountains in New Mexico, or a mixing age between the older and younger populations. It is unclear if all the zircon from the quartzite is detrital, or whether some grew during metamorphism in the quartzite. In general, Th/U ratios for ~1.44 Ga zircon is <0.1, possibly suggesting metamorphic growth, while for ~1.81-1.55 zircon they are both <0.1% and >0.1%, suggesting a detrital origin. However, zircon in both age groups exhibit a variety of textures and shapes. Some ~1.44 Ga zircon grains are euhedral and exhibit oscillatory zoning, some display narrow overgrowths, and others are anhedral with no zoning. The variety of textures and morphologies of ~1.44 Ga zircon suggest that this population is detrital, and that the quartzite was deposited and metamorphosed after. In-situ LA-ICP-MS U-Pb analysis was carried out on monazite from four biotite schist samples to constrain the metamorphic history. The monazite yielded ~1.73 Ga and ~1.42 Ga age populations, and separate populations that show ~1.67-1.48 Ga and ~1.39-1.34 Ga age spreads. The ~1.73 Ga and ~1.67-1.48 Ga populations may be detrital or metamorphic. Monazite ages between ~1.6 Ga and ~1.5 Ga may be due to the mixing of age domains or Pb loss, because metamorphism during that time has not been recognized in Laurentia. The ~1.42 Ga and ~1.39-1.34 Ga populations are most likely metamorphic for two reasons. First, if the biotite schist experienced metamorphism after the Mesoproterozoic there would be evidence for a younger metamorphic event. Second, a monazite inclusion in garnet yielded 1416 ± 85 Ma and 1355 ± 86 Ma ages, indicating that garnet grade metamorphism occurred at or after ~1.4 Ga. Deformation and metamorphism at ~1.42-1.34 Ga is consistent with the age of the Picuris orogeny. Thus, based on the likely <~1.44 Ga deposition of sedimentary rocks in the southern half of the Mt. Evans 7.5-minute quadrangle, and on the ~1.42-1.34 Ga ages of folding and metamorphism it is concluded that the Picuris orogeny caused penetrative deformation and metamorphism in this part of Colorado.
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Simulation of high water-cut in tight oil reservoirs during cyclic gas injectionData from a pilot test imply substantial water production after gas injection, which may impede oil production, but the underlying mechanisms are poorly understood. A compositional model is developed to study possible mechanisms for high water-cut pilot results. First, eight pseudo-components were used to match the PVT lab results of a typical oil sample from the Wolfcamp shale. A lab-scale model was then established in our simulation study to match the results of gas huff-n-puff experiments in cores, in which key parameters were identified and tuned. A half-stage field model consisting of five fractures was built, where stress-dependent permeability was represented by compaction tables. In addition, a sensitivity analysis was conducted to examine the roles of different mechanisms behind the abnormal high water-cut phenomenon. Our simulation results have shown that initial water saturation, IFT-dependent relative permeability, reactivation of water-bearing layers, and re-opening of unpropped hydraulic fractures may affect the water-cut after gas injection. Among them, re-opening of unpropped hydraulic fractures was the most critical factor. This study also optimized the period of injection and soaking phases and well bottom-hole pressure to improve the economic benefits of production operations.
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Applied machine learning for multi-sensory robot perceptionIn recent years, advances in autonomous robotics have begun to transform how we work and live. Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles are helping us to deliver goods, conduct surveys of construction sites, and perform search and rescue alongside first responders. However, designing robots with this level of autonomy is often challenging due to the complexity of the real-world environment. Multi-sensory perception is a critical component to address this challenge and develop robust autonomous robotic systems. By combining multiple inputs from sensors, the system can eliminate a single point of failure from sensor degradation and generate new insights to make better decisions integrating information from dierent sensor modalities. Recent breakthroughs in Machine Learning, especially the Deep Neural Network(DNN) based deep learning perception pipelines have been proven effective in a number of robot perception tasks. However, the significant computation cost for Deep Neural Networks is prohibiting their deployment on a robot system with limited power budget and real-time performance requirement. It is important to bridge this gap by optimization to deploy state-of-the-art machine learning models to a real-world robot systems. This work investigates the viability to develop robust multi-sensory robot perception systems enhanced by machine learning models in three different chapters. First, I explore the effectiveness of DNN perception pipelines in object detection and semantic segmentation tasks, then experiment on various model optimization techniques to enhance the efficiency of these perception models, achieving real-time performance on robot system with a limited power budget. Then I elucidate the design and implementation of a thermal sensing robot system that performs sensor fusion of a thermal camera and an RGB-Depth Camera to automatically track occupants in a building, measuring their forehead temperature, providing fine-grain information for better decision making in intelligent Air Conditioning (AC) system. Finally, I explore camera pose estimation using rectangular to spherical image matching, enabling a robot to quickly grasp a scene with spherical camera, and allow other robots to localize themselves within the scene by matching rectangular sensor images to the spherical image.
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Performance and cost-effectiveness of commercially available adsorptive technologies for treatment of per- and poly-fluoroalkyl substances (PFAS) impacted groundwaterPer- and polyfluoroalkyl substances (PFAS) are emerging environmental contaminants that have received significant recent attention due to their relatively common occurrence in the environment, recalcitrance in treatment systems and potential adverse human health impacts. While there are currently no maximum contaminant levels established by the United States Environmental Protection Agency for PFAS chemicals, social awareness has motivated the Department of Defense, manufacturing companies, and water providers to address the presence of these contaminants in drinking water supplies by using advanced treatment techniques. Currently, the complexity and costs associated with treating PFAS are barriers for widespread implementation of a remedial practice. Consequently, the primary goals of this study were to evaluate the performance of a novel adsorptive media compared to existing PFAS treatment technologies and develop a decision support tool to aid in PFAS treatment selection based on performance and cost.
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Hydrothermal solubility of monazite rare earth element endmembers, TheMonazite-(Ce), a light rare earth element (REE) phosphate, occurs as an accessory mineral in metamorphic, igneous, and sedimentary environments, and is a common ore mineral in hydrothermal REE deposits. The chemical composition of monazite has proven useful as a geochronometer and geothermometer, but currently there is no model describing the compositional variations of REE in monazite resulting from interactions with hydrothermal fluids. To develop such a model requires quantification of the chemical properties of the aqueous REE species and the solid solution properties of the mineral phase. The thermodynamic properties of monazite endmembers have been determined previously using calorimetric methods and low temperature (< 100 °C) solubility studies, but only a few solubility studies have been conducted at hydrothermal conditions. In this study the solubility products (Ks0) of LaPO4, PrPO4, NdPO4, and EuPO4 monazite endmembers have been measured at temperatures between 100 and 250 °C at saturated water vapor pressure (swvp), and are reported according to the reaction: REEPO4 = REE3+ + PO43-. The REE phosphates display retrograde solubility, with the measured Ks0 values varying four to five orders of magnitude over the experimental temperature range. A comparison of the solubility products determined in this study to those calculated from the available calorimetric data for monazite combined with the properties of the aqueous REE and phosphate species, show that there are discrepancies between the experimental and calculated values. Within the experimental temperature range, the differences between the calculated and measured Gibbs energy of reaction (ΔrG0) for PrPO4, NdPO4, and EuPO4 increase with higher temperatures (up to 15 kJ mol-1 at 250 °C), whereas for LaPO4 these differences in ΔrG0 increase at lower temperature (up to 8 kJ mol-1 at 100 °C). To reconcile these discrepancies, the enthalpy of formation of monazite was optimized by fitting the experimental solubility data and extrapolating these fits to reference conditions of 25 °C and 1 bar. The optimized thermodynamic data provides the first internally consistent dataset for all the monazite endmembers, and can be used to model REE partitioning between monazite and hydrothermal fluids. These data are implemented in a thermodynamic database available to a wide community and can be used to model monazite stability for a variety of geological and engineering applications.
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Estimating mineral distribution using machine learning modelsEstimating mineral distribution is important in mine planning. In this research, we studied how to apply tensor completion methods to estimate mineral distribution from partial observations. This includes how to map a partial observations as boreholes data format into tensor format, which problems should be considered to estimate mineral distributions, how to deal with these problems by modifying existing tensor completion methods. Our research showed capability to estimate mineral distributions from sparse and irregularly distributed observations.
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Extracting biological insights from single molecule measurements of protein conformational dynamics using K-SVD algorithmThe conformational changes that biological macro-molecules undergo play a crucial role in functionality. These changes are measurable at the single molecular level using a technique known as single molecule Förster resonance energy transfer (smFRET). By measuring conformational changes at the single molecule (SM) level, distributions of physical properties, such as dwell times of the stepping motion observed in molecular motors, can be statistically modelled and monitored over time. However, there are several sources of error that are introduced when recording smFRET data. The recorded data typically have a low signal-to-noise ratio (SNR) and the number of conformations and transitional rates need to be estimated from this noisy data. Common software packages are available to analyze smFRET data, estimating these parameters and have some success in applications to data where the power of the noise is much less than that of the underlying signal. As the SNR is lowered, these packages naturally produce less accurate parameter estimations. In this thesis, we investigate the denoising ability of the K-SVD algorithm, which computes the singular value decomposition (SVD) of transform coefficients to improve the reconstructive abilities of the atoms in a transform dictionary. A dictionary is learned through varying training data parameters until an optimal dictionary is produced. Simulated data is then approximated and analyzed after projecting the data to a sparse subspace determined by the learned dictionary. The approximations from the K-SVD allow accurate estimation of the number of conformations in the data and the respective smFRET efficiencies. The kinetic rate estimates are less reliable due to approximation errors.
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Impacts of rare earth elements on biological wastewater treatment processesRare earth elements (REEs) underpin a host of technologies that are central to modern society, including portable electronics, renewable and clean energy components, medical devices, and military equipment. Obtaining purified REE end products involves numerous physical and chemical processes, many of which generate large volumes of waste that, if not handled appropriately, can cause significant environmental, economic, and social impacts. In some cases, industrial operators involved in the processing or recycling of REEs may seek to discharge REE-impacted wastewater to municipal water resources recovery facilities (WRRFs). However, little is currently understood about the potential for REE-containing wastewater to affect the performance of conventional wastewater treatment processes, particularly biological treatment processes. The study described in this thesis was developed to investigate impacts of REEs on the mixed microbial communities that treat wastewater. To this end, a series of experiments were developed to evaluate multiple biological wastewater treatment components and REEs. To evaluate and visualize potential inhibition thresholds of REEs, culture plates containing varying concentrations of REEs were prepared and inoculated with activated sludge collected from a local WRRF. Concurrently, bench-scale bioreactors were developed to mimic aerobic activated sludge and anaerobic sludge digester conditions in full-scale WWRFs. Aerobic and anaerobic test reactors were treated with high-priority REEs as salts and chelated compounds, and wastewater treatment performance parameters were tracked to evaluate potential impacts from REEs. Additionally, DNA was extracted from sludge samples collected from the aerobic bioreactor system for high-throughput sequencing to evaluate microbial composition shifts as a function of REE treatment. Aerobic culture plate experiments indicated toxicity of europium, gadolinium, lanthanum, neodymium, and yttrium to activated sludge inocula when present at concentrations of 270-400 mg/L. Additionally, clear inhibition to nitrification processes from gadolinium and yttrium salts were identified in the aerobic reactor experiments. Impacts to nitrification performance measures were supported by microbial community analysis, which indicated distinct changes to community composition as a function of REE treatment. In contrast to findings identified in the aerobic reactor system, anaerobic bioreactor experiments did not reveal meaningful impacts to digestion performance measures from the REE treatments that were investigated. However, >97% of unchelated REE treatments were bound up in bioreactor solids. Taken together, our experimental results do not support the viability of allowing concentrations > 10 mg/L of REE to enter WRRFs, which has important implications as public and private entities weigh the benefits and costs of varying REE processing and disposal technologies.
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Hidden Markov approach for screenout detection in unconventional reservoirs, AHydraulic fracturing has gained its popularity all over the world as more tight geologic formations are developed economically for hydrocarbon resources. While exploring for new unconventional resources such as shale plays, horizontal drilling and multi-stage hydraulic fracturing are required to stimulate the geologic units to increase well production. However, due to the stages' operating complexity, different kinds of disruptions in fracturing operations may occur and even result in great economic loss. Screenout is one of the issues caused by the blockage of proppant inside the fractures. In this project, a horizontal well landing in the Niobrara B shale, Denver-Julesburg (DJ) Basin, is simulated with multiple fracturing stages in a hydraulic fracturing software and various synthetic fracturing treatment data are forward modeled for both screenout and non-screenout scenarios. This thesis describes a screenout classification system based on Gaussian Hidden Markov Models, trained on simulated data, in order to predict screenouts and provide early warning by learning pre-screenout patterns in the simulated surface pressure signals. The classification system consists of two Gaussian Hidden Markov Models (screenout and non-screenout), each of which is fitted and optimized by its respective training set. Both Hidden Markov Models are assigned with two 1D Gaussian probability density functions to represent the distribution of their associated simulated surface pressure signals. During the classification process, once a new surface pressure sequence is observed, the log likelihood is calculated under both fitted models and the model with greater likelihood will be predicted as the class of this new observation. The classification system is validated and verified with a hold-out testing data set from the simulations and the statistics of the performance is presented in a confusion matrix. The results indicate the classification system achieves 86% accuracy for successfully predicting screenout events around 8.5 minutes prior to screenout occurring in the simulation. The described methodology is demonstrated to be a useful tool for early screenout detections and shows its promising feasibility of other time-series analysis such as microseismic data.
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Regulation, product durability, and market process in recycling electronic wasteThis is a three-paper dissertation in fulfillment of the requirements of a Ph.D. in Mineral and Energy Economics at the Colorado School of Mines. It is set in a broader economic context surrounding electronic waste, with public policy decisions and market process in durable wastes---inclusive of critical materials. Unifying themes include durable goods, institutional choice, critical mineral scarcity, and evolving economic thought on waste management. The first paper is an empirical study of state adoption of extended producer responsibility (EPR) laws. The second (co-authored with advisor Daniel Kaffine) is a neoclassical analysis of the potential for unintended consequences (backfire) in durable recycling subsidy policy. The third is an advancement in modeling the role of transaction costs and disposal (as supply) uncertainty in reverse supply chain networks by way of network equilibrium.
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Time-lapse study of microseismic velocity and reflection imaging, AThe unconventional oil and gas industry is convinced that fractures are one of the main differentiators between a good and bad well. Though, fracture delineation within unconventional reservoirs still bares substantial uncertainty. Well logs and surface seismic data try to characterize areas in an effort to populate fracture networks. Unfortunately, logs represent near-wellbore phenomena and 3D seismic data have inherently low frequency content; you are limited to a scale of faulting that can be seen at this resolution. Microseismic data are commonly used to locate induced fractures, but their naturally high temporal frequency content can create reflection images of these “Discrete Fracture Networks” (DFNs), effectively boosting resolution beyond what is observed in 3D seismic. The goal of this research is to use time-lapse microseismic data and image the microseismic reflection information to produce a much higher resolution of the reservoir target area. In addition, we can take this information and propagate it geostatistically throughout a 3D seismic volume, in hopes of achieving better regional insight into the mechanisms that control hydraulic fracturing. Two wells were hydraulically fractured in the Niobrara in 2010 followed by a well placed in the deeper Greenhorn area and subsequently completed in 2014. Vertical distance between the Niobrara and Greenhorn well targets is 500 feet. A vertical monitor well (Bevo) was placed in the center of these treatment wells to maximize coverage (vertically straddling each formation and monitoring as close as 300 feet away) along the mid-lateral area of each well. Research data surrounding these wells consist of 3D seismic, microseismic and well log data in the Hereford Field, Northeastern Colorado, in the Denver-Julesburg Basin. Analyses of time-lapse microseismic data were performed on the Longhorn B3 (Niobrara), B5 (Niobrara) and G4 (Greenhorn) horizontal wells, monitored from a geophone-array in the Bevo vertical well. Microseismic event locations from the G4 completion show propagation upward to the B5 well, indicating preferential growth towards potential depletion of the overlying Niobrara. 3D seismic data show faulting in the area, suggesting hydraulic fracture conduits. Microseismic reflection imaging displays smaller scale fractures coinciding with larger faults in the area. Time-lapse velocity results showed that, over a 4-year time period, significant velocity changes were observed, suggesting that a combination of depletion, pore-pressure and fracture network changes have occurred. The x- and y- dimensions of the microseismic image volumes are 700 x 700 feet, but the determined reliable microseismic imaging area is 300 x 300 feet, due to the geometry of the experiment and associated Kirchhoff depth migration artifacts. This research not only proves that we can achieve higher resolution reservoir insight, but it also tells us that there are many more variables involved and they should be considered when drilling and completing future wells.
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Integrating big data analytics and cybersecurity for power distribution networks with distributed energy resourcesThe legacy electric power distribution system is designed primarily for unidirectional power flow from the electric grid made of large rotating electric machine-based generators located away from the load centers through highly interconnected transmission and sub-transmission network and radial distribution systems. The legacy distribution network is not designed to handle multiple sources of distributed energy resources (DER) such as different forms of photovoltaic (PV) systems, electric vehicles (EV), storage and wind power. However, the future grid is transitioning to system with bidirectional power flow at the distribution level with increasing penetration of DERs. DER penetration level is increasing drastically, in particular PV and storage, in distribution system and is expected to grow in excess of 50% within few decades. The monitoring and efficient control of such DERs requires data and communication that allows the integration of DERs with the modern grid. It also brings vulnerabilities for cyber-attacks that can have a negative impact on overall electric power system operation. In order to design a cost-effective communication system for a smart distribution system it is necessary to consider the cyber-physical relationships as well as the big data analytics and cybersecurity features of such design. Those aforementioned features should be included in the early stages of ICT design. In order to address the new challenges of ICT design for DERs this research studies different approaches on how to integrate big data analytics and cybersecurity to the traditional ICT design approaches. One of the major contributions of this research is the development of a highly detailed cyber-physical communication model platform done in Network Simulator 3 that can be used for simulations of advanced metering infrastructure (AMI). This simulation platform can be used to test and to validate new cybersecurity and data analytics tools of an information and communication technology design for smart distribution system. Other contributions include (a) a comprehensive study on big data analytics (BDA) for distributed energy resources, based on both extensive literature review and mathematical equations for analyzing the amount of data increase in observed data from power quality analysis at distribution level. (b) a set of cybersecurity recommendations at device level for improving the overall cybersecurity of distributed energy resources, based on the literature review together with the previous research done at the National Renewable Energy Laboratory (NREL).
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Removal of arsenic and antimony from complex lead bullion via vacuum distillation, TheVacuum distillation is based on the selective evaporation of volatile impurities from liquid melts and has been extensively studied. It is known to provide better operation conditions and a better control of product composition. In order to understand the thermodynamics of vacuum distillation, it is essential to know the activity coefficient of the impurity in the melt. In this research, three thermodynamic models were used to calculate the activity coefficient. The models are the molecular interaction volume model (MIVM), the Wilson equation, and the non-random two liquids (NRTL) model. The research focused on the determination of binary parameters for the Pb-As and the Pb-Sb binary systems and the prediction of vapor-liquid (VLE) necessary to understand the removal of arsenic and antimony from their binary lead alloys under reduced pressure. Vacuum distillation experiments were conducted on Pb-As and Pb-Sb alloys by varying distillation pressure, temperature, time, and the alloy composition. It was discovered that the removal of arsenic increased with a decrease in distillation pressure, and an increase in distillation time and temperature. The removal was a strong function of temperature; at 5 Pa, 650 °C and 45 min, 79.2wt. % removal extent was achieved, and arsenic content decreased from 2.46 wt. % to 0.53 wt. %. It was discovered that antimony removal also increased with a decrease in distillation pressure, and an increase in distillation time, and temperature. At 5 Pa, 700°C, and 90 minutes, 38.6 % antimony was removed and its content was decreased from 3.5 wt.% to 2.2 wt. %. It is noted however, that antimony removal required higher temperatures processing time for its removal compared to arsenic. The research main’s contribution to vacuum distillation is the VLE prediction for the removal of arsenic.
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Design of a wireless energy harvesting system with a high polarization-purity circularly polarized arrayAs the search continues for alternate forms of energy sources, one that shows great promise and feasibility is wireless energy harvesting (WEH), which is the ability to harvest radio frequency (RF) energy from ambient or dedicated sources. Wireless energy harvester systems aren’t known for harvesting large amounts of power, however, with the development of the Internet of Things (IoT), WEH technology is seen as a great source of energy for low power wireless sensors in IoT applications due to their ease of implementation and low cost. The purpose of this work is to design a WEH system at Wi-Fi band (2.45 GHz) and examine its efficiency to harvest RF energy from ambient and dedicated sourced. The system developed consists of a circularly polarized receiver patch antenna which uses a U slot configuration to achieve the desired polarization as well as a wide bandwidth, and a half wave rectifier circuit with an integrated matching network. For more efficient harvesting, a sequentially rotated 22 planar array using U-slot patch antenna elements is also designed. The antenna array not only increases the receiver gain compared to a system with a single receiver antenna, it also significantly improves the polarization purity of the receiver and thus the overall efficiency of the system. The above subsystems as well as a two-stage Dickson multiplier circuit is designed, simulated, and analyzed in CAD software. The design process for the aforementioned WEH system aims to lay a foundation for future work in the area of WEH so that the need for batteries and power cords in IoT sensor systems can be eliminated.
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Target-oriented imaging of acoustic media using unknown and uncontrolled random sourcesTarget-oriented imaging seeks to localize inhomogeneities within a medium from measurements of the waves that scatter off them. Conventionally, this requires both knowledge and control of the sources used to illuminate the scattering targets. In this thesis, I explore and develop methods for imaging arbitrarily complex acoustic scatterers that require neither knowledge nor control of the sources of illumination. Inspired by random-illumination imaging experiments in optics, I first provide a geometrical explanation of the memory effect, a phenomenon in which the interference of scattered waves from a random medium preserves information carried by the wave incident to the medium. I simulate the time dependence of the memory effect using short-duration impulses that transmit through a collection of random point scatterers. Next, I demonstrate the ability to image strongly scattering targets in the presence of unknown and uncontrolled random sources. The linear sampling method is used to invert the total recorded waveforms and obtain an image of the targets. Successful imaging under such conditions requires the persistent radiation of scattered energy that can be amplified and detected in the recorded data. Subsequently, I introduce an imaging method based on inverting the Lippmann-Schwinger equation of acoustic scattering theory. I compare the proposed Lippmann-Schwinger inversion with the linear sampling method and explore their physical bases. Numerical experiments are performed to quantitatively assess the two methods. Finally, I resolve the dependence of the linear sampling method on an ambiguous time parameter and establish a physical framework for the method's interpretation. Numerical algorithms are given to properly and efficiently implement the method in both the time and frequency domains. I validate the algorithms and interpretation of the method with numerical examples.
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Terrestrial and satellite radar interferometry applications for ground deformation investigations in urban subsidence detection, landslide velocity monitoring, and novel failure discoveryThe projects present interferometric radar measurements at high temporal resolutions and fine spatial precisions that allow new insights about ground deformation dynamics. The work is organized into three studies: (1) A ground-based interferometric radar (GBIR) monitoring campaign conducted on a slow-moving, translational failure landslide in Granby, Grand County, Colorado, USA. (2) A terrestrial radar interferometry (TRI) monitoring campaign for detecting ground settlement analysis within an urban setting in Seattle, Washington, USA. (3) A case study of novel landslide activity recognition related to a very slow creep landslide using satellite ALOS-1 radar interferometry. The research presents methods of survey planning, line of sight measurement, spatial and temporal filtering, uncertainty and error budgeting, scene geocoding, and spatial frame correction. Results of these studies inform hazard assessment and mitigation activities, novel landslide detection and feature recognition, and sub millimeter velocity monitoring of ground deformation dynamics. Independent datasets of deformation for verification and comparison of movement monitoring with discussion regarding the capabilities and limitations of radar measurements to characterize deformation in these environments. Results are used to create radar-supported workflows for achieving geotechnical engineering objectives including submillimeter velocity tracking, near real-time processing and results, and unsupervised reconnaissance campaigns for novel landslide detection.
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Robot learning for loop closure detection and SLAMRobotics and autonomy continues to be a key research and development focus around the world. Robots are increasingly prevalent in everyday life. From manufacturing, home cleaning, to self-driving vehicles, robots are an ever-present reality with demonstrated ca- pability to increase quality of life for humans. As more and more robots exist surrounding humans, it becomes increasingly critical that robots can accurately sense and reason about the environment. The functionality of a robot building a map of its environment and lo- cating itself constantly within the map is known as Simultaneous Localization and Mapping (SLAM). SLAM is a difficult problem, and can be especially challenging when environmental appearance changers occur or when a GPS signal is not available. However, it’s within these challenging environments where the use of robots is critical. Consider a partially collapsed underground mine environment. If the environment is potentially dangerous, it doesn’t make sense to risk human life to enter the mine to perform search and rescue. If robots can be enabled to operate in challenging environments such as collapsed mines, human life can be saved. This Master’s thesis addresses the problem of increasing the effectiveness of SLAM in these challenging environments. First, I describe a data structure capable of capturing environmental metadata for semantic description overlay to augment mapping capability. Secondly, I introduce a novel loop closure detection technique that utilizes robot learning to understand complex environments. These efforts combined contribute to increasing the effectiveness of SLAM in GPS-denied environments or environments with varying lighting conditions.
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Geology and geochemistry of the Kansanshi Cu-(Au) deposit, North-Western Province, ZambiaThe Kansanshi Cu-(Au) mine exploits a structurally controlled deposit hosted by strongly deformed Neoproterozoic metasediments in the Katangan Basin of southern Africa. It is currently the largest active Cu mine in Africa, with a total endowment of nearly 12 Mt of Cu and 7 Moz of Au. Copper occurs predominately as coarse chalcopyrite in large quartz-carbonate-pyrite vein swarms and as finer-grained disseminations in carbonaceous phyllite and schist wallrocks. The veins and associated disseminated sulfides are concentrated in domes along the crest of the NW-SE trending Kansanshi antiform. The deposit occurs within the Domes region of the Central African Copperbelt, where amphibolite facies metamorphism and recumbent folding have made correlating between local and regional stratigraphy problematic. Between 2013 and 2015, over 100 deep exploration drill holes were completed across the Kansanshi mining lease with the aim of resolving outstanding questions surrounding the stratigraphic position of the deposit, its structural evolution, and controls on the distribution of ore. This study utilizes geological and geochemical data from the drill holes and geologic mapping in the open pits, along with nearly 600 km of prior drill hole data, to develop a revised structural and stratigraphic interpretation for the Kansanshi deposit. This work reveals a number of major, previously unrecognized, structural and stratigraphic features critical for understanding the genesis of this world-class copper deposit. Metasedimentary rocks at Kansanshi can be divided into three tectonostratigraphic domains separated by evaporitic breccia sequences. The Footwall Domain consists of gneiss and schist basement rocks overlain by >1200 m of quartzite and metaconglomerate of the Basal Clastic Sequence. The Footwall Domain is separated from the overlying Kansanshi Mine domain by the Evaporitic Sequence, a >300 m thick anhydrite-bearing sequence containing multiple polylithic breccia horizons interpreted as the residuum of vanished evaporites. The base of the Kansanshi Mine domain is represented by the Dolomitic Sequence, a homogeneous dolostone interpreted as a shallow water equivalent of shales in the Mwashya Subgroup of the Zambian Copperbelt. The Dolomitic Sequence is overlain by the Grand Conglomérat (Mwale Formation), a Cryogenian metadiamictite that marks the Sturtian “Snowball Earth” glacial epoch. Major geochemical variations in Fe, P, Mn, S, and Ti within the metadiamictite were used to map distinct geochemical facies over a >50 km2 area. The lithogeochemical variations are interpreted to indicate a significant and rapid transition from an initially stratified, anoxic, ferruginous, water column in sub-ice conditions, to a mixed, oxidized water column associated with deglaciation. The Grand Conglomérat is overlain by the Kakontwe Formation, a marble dominated post-glacial ‘Cap Carbonate’ sequence. The Kakontwe Formation transitions upward into the Katete Formation, a rhythmically banded, locally anhydrite-bearing, carbonate-clastic sequence, which acted as a local décollement during deformation. The uppermost stratigraphic unit at Kansanshi and the principle ore host is the Monwezi Formation, a mixed clastic sequence dominated by carbonaceous phyllite and garnet schist. All of these units are recumbently folded within the Kansanshi Mine domain resulting in stratigraphic repetition. The Kansanshi Mine domain is overlain, and locally truncated by, the ‘Mafic-breccia Zone,’ which is an albitized dolomitic breccia horizon containing large mafic meta-igneous blocks derived from the Evaporitic Sequence. These meta-igneous blocks are bound above and below by younger strata, suggesting that they were likely emplaced via diapirism during salt tectonics. The ‘Mafic-breccia Zone’ is overlain by the Upper Plate Domain, which is stratigraphically equivalent to the Kansanshi Mine Domain, but structurally inverted, and interpreted as the bottom limb of a large recumbent fold. The complex structural deformation in the Kansanshi area was associated with basin inversion and metamorphism during the Pan African orogeny. The earliest phase of deformation created nearly orthogonal N- and SE-verging recumbent folds, probably through progressive N to SE deformation. Post-orogenic collapse is interpreted to have resulted in detachment along evaporite horizons, resulting in pervasive boudinage and development of the Kansanshi antiform. Interference folding of pre-existing SE-verging recumbent folds created domes along crest of the antiform. Uplift and cooling during this process was associated with a ductile to brittle transition that resulted in rigid siliciclastic horizons forming boudins within ductile carbonate horizons at all scales. Initial veins developed in boudin necks of siliciclastic lithologies followed by later brittle veining and faulting during the final stage of mineralization. Vein patterns suggest that extensional boudinage controlled the initial vein geometries and introduced ore fluids into chemically favorable reduced carbonaceous host rocks during the waning stages of metamorphism.
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Fundamental electrochemical study on neodymium molten salt electrolysis in fluoride bathIn the recent decades, the clean energy economy has been driving a rapidly increasing demand for rare earth materials with their applications in essential high technologies such as electric vehicles and wind turbines. The technologies for the winning of rare earth metals are developing. As the predominant winning technique, electrolysis of rare earth oxides in molten fluoride systems has been faced with two major problems: one is low energy efficiency and the other is high emissions of perfluorocarbons (PFCs). Therefore, there exists a need for the metallurgy community to address those problems both theoretically and practically, and to develop improved processes for reducing rare earth oxides. This thesis uses the example of neodymium winning to elucidate the fundamental electrochemical properties of the molten fluoride electrolytes and the mechanism of the electrolysis process, and provides a guide to economically win rare earth metals with elevated energy efficiency and decreased emissions for the process. As for the property investigation, this research carried out measurements to determine the liquidus temperatures of the NdF3-PrF3-LiF ternary salt system, the solubilities of Nd2O3 in the electrolytes, and the electrical conductivity of the NdF3-LiF salt. A conductance cell system was developed to investigate electrical conductivity and produce reliable data. The experimental results indicate that the electrical conductivities of the molten NdF3-LiF system between 70 wt% to 85 wt% NdF3 within the range of temperature from 950 °C to 1050 °C range from 4.38 ohm-1 cm-1 to 6.08 ohm-1 cm-1. Furthermore, an empirical equation to estimate the value of the electrical conductivity for a specific molten salt is proposed. A mathematical model regarding the voltage change against current in the molten Nd2O3-NdF3-LiF electrolysis is proposed based on the thermodynamics and kinetics study and validated through experiments and literature observations. The model and the experimental results illustrate that the limiting current of the electrolysis cell increases with the increase of anode surface area, higher energy efficiency can be achieved with reduced electrode distance and more effectively, reduce the submerged depth ratio of anode to cathode. An effective technique to prevent the generation of PFCs is to design the cell conditions which allow the limiting current to be smaller than the critical current.
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Morphology prediction of reactive silver ink systemsThe increase in demand for additive manufacturing technologies necessitates from its potential applications in various fields that could benefit tremendously. Some of these include, the medical field, where doctors can build a model of any damaged body part and analyze it for pre-treatment planning, aerospace industry, where they use rapid prototyping to model structures with a goal to reduce the weight, and even artists use it to explore their creativity, and more recently, printed electronics. Over the last few decades, what originated as rapid prototyping, diverged and evolved to accommodate more specific manufacturing needs. Some of these new technologies such as Stereolithography, Vacuum deposition, Electroless plating, etc. have further evolved to satisfy the time and cost demands of current systems. Drop-on-Demand (DoD) or Inkjet printing is one such technology that can print high resolution features precisely in a timely and inexpensive manner. As mentioned earlier, one area of application that could benefit vastly from DoD printing is printed electronics, specifically optoelectronics such as solar cells which is a $2 billion/ year industry. However, printed electronics have advanced to a point where the demand for a new class of materials with unique properties that target future technologies is extremely. For example, future Silicon Heterojunction (SHJ) solar cells have top-layer Transparent conducting oxides (TCO's) that are extremely temperature sensitive. Therefore, the demand for newer conductive inks that are compatible with these future technologies that require them to be stable at lower temperatures has increased. Recent exploration into silver precursor inks has yielded promising results. For example, silver compounds with carbamate or other relatively low molecular weight ligands (compared to polymer stabilizers) have been synthesized that decompose at temperatures near 150 °C, yielding electrical conductivities approaching that of bulk silver. Unfortunately, even these temperatures render the ink incompatible with many plastic and paper substrates used in flexible electronic and biomedical devices along with the above mentioned Silicon Heterojunction (SHJ) solar cells. Reactive inks are a viable alternative to the current technology that involves screen printing of metallic pastes, offering a low-cost, higher performance alternative to these traditional, particle-based inks. However, one of the major challenges when using reactive inks is their high sensitivity of print morphologies to processing parameters such as substrate temperature, ink composition, thermo-physical properties of solvents, etc. In order to control morphologies of reactive inks, the underlying mechanisms that drive the reaction kinetics must be understood. Currently, very little is know about the mass transport kinetics of printed reactive inks or their impact on morphology and material properties. While numerous models exist to guide the design of colloidal/particle based inks and solute/precipitating inks (e.g., salts, polymers, proteins) so that a desired morphology can be targeted, no models exist for reactive inks that include the kinetics of the reduction reaction, particle nucleation, and particle growth. This work will develop the understanding necessary to control the morphology of printed reactive inks. This outcome will be accomplished by experimentally measuring the chemical and mass flow kinetics of self-reducing silver and copper reactive inks and then correlating those kinetics to the mass and thermal transport phenomena involved in droplet evaporation/evolution so that particle nucleation, growth, aggregation, and chemical sintering can be properly modeled and morphology predicted. The heat and mass transport of both reactants and particles within the droplet will be modeled in multiphysics simulations and combined with kinetic models of the reduction process to predict particle formation rate as a function of fluid properties (surface tension, viscosity) and reduction kinetics (activation energy, concentration, temperature). Overall, this work will improve the quality of materials printed using reactive inks by understanding the mechanisms that contribute to overall morphology and using this insight to design inks, print processes, and post-print processes for broad classes of reactive ink materials (low cost metals, magnetics, oxides). This study will also help develop new knowledge on how ligand selection, reduction activation energy, solvent properties, contact angle, and substrate temperature dictate the physical phenomena that control physical structure.
