This post will be updated as information becomes available.
Grace Beaudoin The Behavior of Halogens (F, Cl, Br, I) in Altered Oceanic Crust during Prograde Subduction Zone Metamorphism and Devolatilization The project, defended on April 18 2022, was supervised by Dr. Jaime Barnes with committee members including Drs. Sarah Penniston-Dorland, Daniel Stockli, John C Lassiter, and Timm John Abstract: To be added
Ricardo De Braganca Enhancing Full Waveform Inversion with Machine Learning Optimizers The project, defended on July 1 2022, was supervised by Dr. Mrinal Sen with committee members including Drs. Nicola Tisato, Stephen P Grand, Zeyu Zhao, and Kyle T Spikes.
Abstract: To be added
Yuquian (Philomena) Gan Shelf-Slope Sediment Transport in Medium-Sized Basin Margin Clinoforms , with a Focus on Slope Channel Facies and Architecture The project, defended on April 15 2022, was supervised by Dr. Cornel Olariu and Dr. Ron Steel with committee members including Drs. Cristian Carvajal, Brian K Horton, and David Mohrig Abstract: To be added
Andrew Gase Seismic Investigations of Subduction and Intra-arc Rifting at the Hikurangi Margin, New Zealand The project, defended on November 16, 2021, was supervised by Dr. Harm Van Avendonk and co-supervised by Dr. Nathan Bangs and with committee members including Drs. Jaime Barnes, Dan Bassett, Kyle Spikes and Nicola Tisato. Abstract: Subduction zones are dynamic systems that control the global distribution of large earthquakes and volcanism. Many interrelated factors can control tectonic, seismic, and magmatic processes within subduction zones, including mechanisms that vary stress, thermal regime, volatile supply, as well as inherited features within the lithosphere, but the relative importance of these factors are debated. North Island New Zealand, where the Pacific Plate subducts beneath the Australian Plate, is renowned for its unique patterns of seismicity and plate coupling in the forearc, the Hikurangi margin, and its magmatically productive intra-arc rift, the Taupo Volcanic Zone. In this dissertation I present three studies that use newly acquired controlled-source seismic data to evaluate (1) crustal and sedimentary controls on seismic behavior in the Hikurangi margin forearc, and (2) interplays between magmatism and crustal deformation in the offshore Taupo Volcanic Zone. In the first study, I explore the crustal structure of the northern Hikurangi margin, which is world renowned for its low seismic coupling, frequent shallow slow slip events,and strong ground-motion amplification during large earthquakes. I show that sharp along- strike variations in frontal accretion indicate variable sediment supply and past subduction of seamounts. Low velocities in the overthrusting plate indicate the presence of compliant materials that likely contribute to tsunamigenesis and enhanced ground motion during earthquakes. In a second study, I compare the structure of the megathrust fault across the interseismic coupling transition between the central and southern Hikurangi margin and reveal a clear correlation between sediment subduction and slip behavior. In the northern and central unlocked, slow slipping segments, the megathrust forms within pelagic carbonates and volcanic sediments. In contrast, the southern locked megathrust is localized to pelagic carbonates and is insulated from the effects of volcanics by ~0.5-1 km of subducting clastic sediment. I propose that slip behavior and coupling is controlled by the lithology and spatial distribution of frictional asperities along the megathrust. Finally, I determine the crustal structure of the offshore Taupo Volcanic Zone and demonstrate that crustal extension and recent magmatic activity are collocated. Deep-penetrating crustal normal faults overlie a ~40-kilometer-wide zone of sill-complexes and heterogeneous seismic velocities in the upper and middle crust. I propose that magmatic intrusions are localized by more permeable fractured crust and contribute to thermal weakening which facilitates rifting.
Zhicheng Geng Deep Learning for Pattern Recognition in Seismic Reflection Data The project, defended on April 19 2022, was supervised by Dr. Sergey Fomel with committee members including Drs. Luming Liang, Mrinal K Sen, Omar Ghattas, and Xinming Wu. Abstract: Pattern recognition plays an important role in analyzing seismic reflection data, which contains valuable information of the subsurface geological structures, and serves as a powerful method for hydrocarbon exploration. Conventional seismic pattern recognition methods commonly involve handcrafted seismic attributes or filters that do not apply to seismic data with complex structures. On the other hand, with new seismic acquisition techniques and equipment providing an increasing amount of data, conventional methods tend to be inefficient in processing large-scale and high-dimensional datasets. Over the past decade, the improvement of computer powers and software development has promoted deep learning as an efficient and effective tool for pattern recognition, which extracts features directly from data without relying on assumptions. This dissertation presents deep learning methods for pattern recognition in seismic reflection data from various perspectives. First, I introduce a semi-supervised learning framework for salt segmentation to alleviate the burden of preparing a large amount of labeled training data. The unsupervised consistency loss enforces the convolutional neural network (CNN) to extract essential information from labeled and unlabeled data, leading to more accurate segmentation results and better generalization ability on different datasets than the supervised learning baseline. Second, I formulate relative geologic time (RGT) estimation as a regression problem and design a U-shape CNN to solve this problem. The encoder-decoder architecture with skip connections results in accurate RGT predictions directly from seismic images. Although trained on a synthetic dataset, the network generalizes well to complex field data. Third, I propose an unsupervised learning method for seismic random noise attenuation. In the proposed method, a convolutional autoencoder is trained to reconstruct clean images from noisy seismic images without supervision from labeled data. The network training is constrained by local orthogonalization loss for better signal and noise separation. Next, I apply CNNs to reconstruct subsurface velocity models from common-image gathers (CIGs), which involves depth-to-depth mapping. The focuses or the flatness of seismic events in CIGs contain valuable information about the surface velocity model. Trained with synthetic dataset migrated using wrong velocity models, the network learns the relationship between the incorrect positioning of seismic energy in CIGs and the corresponding correct velocity update. In the next chapter, I explore the possibility of employing a different network architecture, Transformers, for velocity model building. In the proposed method, velocity models are directly estimated from raw recorded seismic reflection data using a variant of Vision Transformers specially tailored for FWI (FWIT), consisting of an encoder and a decoder. The encoder of FWIT learns to extract high-level information from input shot gathers, which is further analyzed by the decoder to estimate the velocities based on the attention mechanism. The ability to learn long-dependency and the flexibility of predicting variable-length output make Transformers a more suitable architecture for FWI than CNNs. Finally, I discuss and suggest possible future research topics.
Harpreet Kaur Improving Accuracy And Efficiency Of Seismic Data Analysis Using Deep Learning The project, defended on April 12 2022, was supervised by Dr. Sergey Fomel with committee members including Drs. Raymond Abma, Mrinal K Sen, George Biros, and Kyle T Spikes. Abstract: The ultimate goal of seismic data analysis is to retrieve high-resolution information about the subsurface structures. It comprises different steps such as data processing, model building, wave propagation, and imaging, etc. Increasing the resolution and fidelity of the different seismic data analysis tasks eventually leads to an improved understanding of fine-scale structural features. Conventional implementation of these techniques is computationally intensive and expensive, especially with large data sets. Recent advances in neural networks have provided an ability to produce a reasonable result to computationally intensive and time-consuming problems. Deep neural networks are capable of extracting complex nonlinear relationships among variables and have shown efficacy as compared to conventional statistical methods in different areas. A major bottleneck for seismic data analysis is the tradeoff between resolution and efficiency. I address some of these challenges by implementing neural network based frameworks. First, I implement a neural network based workflow for stable and efficient wave extrapolation. Conventionally, it is implemented by finite differences (FD), which have a low computational cost but for larger time-steps may suffer from dispersion artifacts and instabilities. On the other hand, recursive integral time extrapolation (RITE) methods, especially the low-rank extrapolation, which are mixed-domain space-wavenumber operators are designed to make time extrapolation stable and dispersion free in heterogeneous media for large time steps, even beyond the Nyquist limit. They have high spectral accuracy; however, they are expensive as compared to finite-difference extrapolation. The proposed framework overcomes the numerical dispersion of finite-difference wave extrapolation for larger time steps and provides stable and efficient wave extrapolation results equivalent to low-rank wave extrapolation at a significantly reduced cost. Second, I address wave-mode separation and wave-vector decomposition problem to separate a full elastic wavefield into different wavefields corresponding to their respective wave mode. Conventionally, wave mode separation in heterogeneous anisotropic media is done by solving the Christoffel equation in all phase directions for a given set of stiffness-tensor coefficients at each spatial location of the medium, which is a computationally expensive process. I circumvent the need to solve the Christoffel equation at each spatial location by implementing a deep neural network based framework. The proposed approach has high accuracy and efficiency for decoupling the elastic waves, which has been demonstrated using different models of increasing complexity. Third, I propose a hyper-parameter optimization (HPO) workflow for a deep learning framework to simulate boundary conditions for acoustic and elastic wave propagation. The conventional low-order implementation of ABCs and PMLs is challenging for strong anisotropic media. In the tilted transverse isotropic (TTI) case, instabilities may appear in layers with PMLs owing to exponentially increasing modes, which eventually degrades the reverse time migration output. The proposed approach is stable and simulates the effect of higher-order absorbing boundary conditions in strongly anisotropic media, especially TTI media, thus having a great potential for application in reverse time migration. Fourth, I implement a coherent noise attenuation framework, especially for ground-roll noise attenuation using deep learning. Accounting for non-stationary properties of seismic data and associated ground-roll noise, I create training labels using local-time frequency transform (LTF) and regularized non-stationary regression (RNR). The proposed approach automates the ground-roll attenuation process without requiring any manual input in picking the parameters for each shot gather other than in the training data. Lastly, I address the limitation of the iterative methods with conventional implementation for true amplitude imaging. I implement a workflow to correct migration amplitudes by estimating the inverse Hessian operator weights using a neural network based framework. To incorporate non-stationarity in the framework, I condition the input migrated image with different conditioners like the velocity model and source illumination. To correct for the remnant artifacts in the deep neural network (DNN) output, I perform iterative least-squares migration using neural network output as an initial model. The network output is close to the true model and therefore, with fewer iterations, a true-amplitude image with the improved resolution is obtained. The proposed method is robust in areas with poor illumination and can easily be generalized to more-complex cases such as viscoacoustic, elastic, and others. The proposed frameworks are numerically stable with high accuracy and efficiency and are, therefore, desirable for different seismic data analysis tasks. I use synthetic and field data examples of varying complexities in both 2D and 3D to test the practical application and accuracy of the proposed approaches.
Kevin Meazell Deepwater Methane Hydrate Characterization in the Gulf of Mexico: Sedimentology and Stratigraphy The project, defended in Fall 2021, was supervised by Dr. Peter Flemings with committee members including Drs. Jacob Covault, David Mohrig, and Lori Summa. Abstract: Gas hydrate is found in cold, high-pressure, marine sediments around the world. Hydrate is important as a carbon sink, a natural geohazard, and a valuable economic resource. I use classic sedimentologic analyses, well log analysis, X-ray CT, seismic stratigraphy, pore pressure estimation, and basin modeling to elucidate the geologic conditions within highly-saturated, natural gas hydrate reservoirs in the deepwater northern Gulf of Mexico. I begin with the characterization of the channel-levee hydrate reservoir in GC-955 with grain size experiments, lithofacies mapping. Hydrate is found in thin-bedded layers of sandy silt that increase in net-to-gross and mean grainsize downhole. I use these results to interpret deposition of overbank sediment gravity flows from a deepwater bypass channel as it becomes increasingly confined by the levees it builds. Next, I use 3D seismic data to identify the relationship between similar channel-levee systems and venting seafloor gas mounds in the Terrebonne Basin of the Walker Ridge protraction area. I estimate the pore pressures, and show that below the hydrate phase boundary, free gas in the levees builds to a critical pressure and creates hydraulic fractures to the seafloor. I describe a conceptual model by which the venting process perturbs the hydrate stability zone, leading to further venting from shallower positions and the formation of distinct rows of gas mounds on the seafloor. Finally, I combine geomechanical properties of the GC-955 reservoir with the structure of the Terrebonne Basin system to show that the pressure estimates are well within reason. Together, these studies provide new insights into where hydrate is found, and how hydrate systems can both control and in turn be controlled by fluid flow, pressure, and stress in the deepwater environment.
Paul Morris Modeling the Architecture and Dynamic Connectivity of Deep-Water Channel Systems Using a Forward Stratigraphic Model The project, defended on April 21 2022, was supervised by Dr. David Mohrig and Dr. Jacob Covault with committee members including Drs. Richard Sech, Zoltan Sylvester, and Timothy A Goudge Abstract: Deep-water channels are important conduits of terrigenous material to continental margins, and they act as significant reservoirs of natural resources in the subsurface. This dissertation investigates the evolutionary development of deep-water channel systems and their deposits through use of a simple forward stratigraphic model and a seismic-reflection dataset. We apply our learnings to subsurface modeling to ascertain the impact of these architectures on dynamic connectivity. Firstly, we study a high-resolution 3D seismic-reflection dataset of a 25 km reach of the Joshua deep-water channel system in the eastern Gulf of Mexico. We document features analogous to meandering fluvial systems where an initial relatively straight channel underwent systematic bend expansion and downstream translation that resulted in a cutoff at one bend. Channel sinuosity continually increased throughout channel aggradation and correlates to a decrease in the average channel slope through time. We propose this may have promoted increasingly depositional turbidity currents and been a control on the system’s aggradation. We use a forward stratigraphic model where vertical channel movements are linked to a modified stream power law via channel slope and show how this honors trends in sinuosity, slope and aggradation observed in the Joshua. Next, we employ a forward stratigraphic model that honors our observations from the Joshua, comprising a meandering channel that migrates vertically through time (i.e., its trajectory). We find that three types of channel trajectory, when combined with realistic meandering processes, can capture common styles of deep-water stratigraphic architecture observed on the seafloor and subsurface. We document the overarching processes controlling the stratigraphic evolution of our channel-belt models and demonstrate how they provide dynamic, three-dimensional insights that can elude static cross-sectional perspectives, such as those observed in outcropping sedimentary rocks. Finally, we apply our learnings to models of the subsurface. Systematic channel migration and bend architectures are not typically captured in conventional modeling approaches. Using a simple well pair, we show how sweep behavior over production timescales can be controlled by bend-cutoff architectures. An increasing number of bend-cutoff architectures between a well pair typically increases the variability in potential flow path lengths, reflected in an increase in measures of dynamic heterogeneity. Though the models are entirely statically connected, it is the interaction of reservoir architecture and well geometry that controls sweep behavior over typical development timescales.
Sebastian Ramiro Ramirez Integrated Stratigraphic and Petrophysical Analysis of the Wolfcamp at Delaware Basin, West Texas, USA The project, defended on February 18 2022, was supervised by Dr. Peter Flemings with committee members including Drs. Nicola Tisato, Athma R Bhandari, Hugh C Daigle, and Charles Kerans. Abstract: Hydrocarbons stored in low-permeability reservoirs, also known as ‘unconventional reservoirs’, represent important energy resources worldwide. Although current technology allows their production at economic rates, there still are numerous production challenges and unknowns regarding their flow behavior. A better understanding on how fluids stored in these reservoirs are drained by the hydraulic fractures after stimulation may help to optimize completion designs and field development plans. This research is an attempt to describe such drainage behavior in the largest oil producing unconventional formation in the World. I investigated the drainage behavior in Wolfcamp reservoirs at the completion scale by integrating stratigraphic and petrophysical analyses with flow modeling. I interpreted the depositional and diagenetic processes that generated three Wolfcamp cores recovered in the central-eastern Delaware Basin, measured the porosity and permeability of distinct lithofacies, and developed simple models to describe flow in these strata. I found that most fluids (~95% of the pore volume) are stored in low-permeability (e.g., < 60 nD) mudstones that I interpreted as hemipelagics and siliciclastics turbidites. Interbedded with these deposits are the low-porosity (~5% of the pore volume) and low-permeability (e.g., < 50 nD) carbonate lithofacies that I interpreted as gravity flow deposits and diagenetic dolomudstones. The carbonate gravity flow deposits, when dolomitized, are up to 2000 times more permeable than the other deposits and represent preferential flow pathways that drain fluids from the low-permeability strata during production. This drainage behavior increases the reservoir upscaled permeability, and therefore production rates, multiple times higher compared to a reservoir consisting of only low-permeability strata. Hence, the presence of these permeable, dolomitized, gravity flow deposits plays a critical role when producing from Wolfcamp reservoirs as they accelerate drainage. These findings are also applicable to other low-permeability formations exhibiting significant permeability heterogeneity.
Benjamin Rendall Regional and Global Controls on Carbonate Factory Composition and Stratigraphic Architecture During Global Icehouse: Examples from the Pennsylvanian, New Mexico and the Pleistocene, Bahamas The project, defended on April 14, 2022, was supervised by Dr. Charles Kerans with committee members including Drs. Steven L. Bachtel, Brian K Horton, David Mohrig and Xavier Janson
Abstract: To be added
Colin Schroeder In-Situ Visualization and Characterization of Mud-Filtrate Invasion and Filter Cake Deposition Using Time-Lapse X-Ray Micro-Computed Tomography (Micro-CT) The project, defended on April 15 2022, was supervised by Dr. Charles Kerans with committee members including Drs. Nicola Tisato and Robert G Loucks
Abstract: To be added
Justin Thompson Exploring Groundwater Recoverability The project, defended in Fall 2021, was supervised by Dr. Michael Young with committee members including Drs. [Jay Banner, Shelia Olmstead, and Daniella Rempe. BEG news item Abstract: Where the depth of groundwater increases, recoverability—the relative ease of pumping—decreases. Understanding how recoverability changes with planned or unplanned changes in depth-to-water is a critical issue for groundwater managers, policymakers, and stakeholders. However, recoverability is generally not well understood, poorly quantified, and detached from local-level groundwater planning and management where decisions are often based on depth-to-water over time. Currently, Texas’ best estimates of recoverability are derived from arbitrary storage volume constraints, rendering unknown the impacts of variable storage and use conditions, or management decisions. Using hydrogeologic and economic constraints to better calculate recoverability, the topic of this dissertation, could provide a more comprehensive approach for long-term water planning. New modeling methods for quantifying recoverability are developed to capture the complex relationship among aquifer storage conditions, well infrastructure, and recoverability by connecting groundwater drawdown solutions with comprehensive cost components (drilling, equipment, and lifting energy). Partially penetrating well effects, which have direct ramifications for both the capacity and economic elements of recoverability, are used to create a novel approach for optimizing well infrastructure that maximizes long-term recoverable yields. In this way, the model has the flexibility to quantify recoverability for deterministic (user specified or existing) well infrastructure or to explore the limits of recoverability with yield-optimized infrastructure. Single well model results reveal that groundwater stored in shallow and unconfined conditions is highly recoverable; physical constraints are more likely than economic considerations to bind yields. Alternatively, recoverability of groundwater stored in deep and confined conditions is more likely bound by economic constraints than physical limitations; yields may be restricted where the cost of dewatering saturated thickness is prohibitively expensive. Yield-optimized infrastructure is then integrated with spatially distributed aquifer data to model maximum recoverable yields for the Carrizo-Wilcox Aquifer in Texas at an initial depth-to-water and a planned change in depth-to-water in order to assess how yields differ between the two. Results show that the capacity constraints upon regionally recoverable yields correlate strongly with aquifer saturated thickness, economic limitations correlate strongly with aquifer depth, and recoverable yields in shallow and unconfined settings are highly sensitive to changes in depth-to-water. Using a unique approach, recoverability solutions developed here are then linked with three disparate data sets providing aquifer characteristics, existing well infrastructure, and user attributes to quantify the socioeconomic impact to existing users (domestic, livestock, and irrigation) of a planned change in depth-to-water. Important disparities are illuminated by specifying the types of these impacts and allocating them to specific user groups. For example, 84% of study area wells designated for domestic use bear only 58% of the total modeled increase in regional pumping costs, while 5% of study area wells designated for irrigation use bear 19% of these costs. Additionally, while affordability of domestic water supply for the majority of the study area population is unimpeded by the planned change in depth-to-water, results suggest that low-income communities may be disproportionately impacted. Finally, results indicate that the distribution of the types of costs (drilling, equipment, and lifting energy) present at initial groundwater production do not necessarily correspond to the socioeconomic impacts of a planned change in depth-to-water. While drilling costs are found to compose 83% of all study area pumping costs at initial production, 56% of the increase in future pumping costs are driven by lifting energy costs. Thus, policy interventions designed to mitigate these impacts are more likely to meet their objectives if they apply these or similar methods, rather than rely on initial production costs.
Anna Turetcaia Aerobic Metabolism of Organic Matter across the Terrestrial-Aquatic Interface through the Lens of Flume Experiments and Models
The project, defended on May 13 2022, was supervised by Dr. Bayani Cardenas with committee members including Drs. Adam Kessler, Daniel O Breecker, Philip C Bennett, and Matthew H. Kaufman
Abstract: To be added
Alison Tune Interactions between Carbon Cycling and Bedrock Weathering in a Forest of the Northern California Coast Ranges The project, defended in Fall 2021, was supervised by Dr. Daniella Rempe and Dr. Philip Bennett with committee members including Drs. Jennifer Druhan, Daniel Breecker, and Bayani Cardenas. Abstract: The overarching objective of this dissertation is to better understand Earth’s carbon cycle by identifying linkages between plant mediated carbon cycling and bedrock weathering processes. Such processes have been extensively studied in soils to identify the magnitudes and dynamics of carbon sources and sinks. However, recent studies reveal the prevalence of root networks that extend beneath soils into bedrock that may also participate in these dynamics. The contribution of roots in bedrock to carbon cycling in the subsurface is presently poorly understood. To identify the processes operating in the deep root zone and their role in mineral weathering and carbon cycling, this dissertation uses specialized instrumentation installed within an actively weathering forested hillslope in the Northern California Coast Ranges. This dissertation documents carbon and water fluxes at high spatial and temporal resolution throughout a 16 m weathering profile underlying a mixed hardwood-conifer ecosystem. These measurements demonstrate that substantial rhizosphere-related respiration occurs in the deeper, bedrock root zone such that shallow, soil respiration dynamics are likely insufficient to capture subsurface carbon processes in forests characterized by rooting into bedrock. Carbon dioxide emission from the ground surface (also termed soil efflux) is typically thought to be controlled by microbial and plant activity in shallow soils, however, this dissertation reveals that the deeper bedrock contributes to these emissions and can account for up to 100% of the efflux during seasonal drought when soils are dry but roots continue to pull water from the deeper bedrock layers. The carbon dioxide produced in the deeper root zone dissolves in water and this water transits downward, acidifying deeper parts of the weathering profile and driving weathering reactions. The flux of reactivity delivered to the deeper weathering profile scales with the flux of water through the weathered bedrock, such that very little dissolved carbon dioxide is transported during drought years and instead remains in place in the root zone. Monitoring through an extreme drought period also revealed shifts in gas dynamics that reflect changes in the carbon source for respiration, indicating that plant water stress has implications for bedrock weathering. Carbon isotope measurements and laboratory incubation experiments reveal that modern, root-related processes are responsible for the observed carbon dynamics. The organic matter contained within the bedrock (termed petrogenic organic carbon) was shown to be oxidized within the root zone and lower unsaturated zone, with rates comparable to that in riverine systems. Rates of petrogenic organic carbon oxidation in the unsaturated zone may be important to climate over long time scales. The results of this dissertation research reveal that roots that penetrate bedrock fractures beneath soils play an important and sometimes dominant role in forest water and carbon cycling with implications for landscape evolution, water quality, and climate feedbacks.
Abstract: We study the continental dynamics of the contiguous U.S. using global mantle flow models. Our study focuses on three aspects: upper mantle horizontal shear and how it forms seismic anisotropy, basal tractions from the sublithospheric mantle, and how they contribute to forming the present day surface topography, and the flow induced surface deformation. We study the significance of multi-layer seismic anisotropy in the eastern U.S., and the role of small scale flow related shear that affects the anisotropy alignment in the western U.S. Our study proposes the importance of understanding the complexity of sublithospheric mantle flow in the western U.S. With predictions of dynamic topography, we present the effect of plate boundary dynamics on the western U.S. flow, and the various levels of support from mantle flow to the western Cordillera and Intermountain Region. We further constrain the lithosphere-sublithosphere interactions with vertical crustal motions. We observe mantle flow contributions to the subduction zone related deformation with along strike variations, reduced mantle flow support at the center of Great Basin, and the uplift at the Sierra Nevada. We propose the next step of research towards a comprehensive analysis using flow predicted deformation and lithospheric heterogeneity
Abstract: Today, Antarctica holds a 58 m (190 ft) sea level potential locked in its grounded ice. Ice shelves serve as a gatekeeper to this grounded ice. However, sea level is currently rising at an alarming rate, ultimately endangering lives and economies all over the world. To accurately project the future sea level in an ever-changing climate requires a deeper understanding of how ice shelves respond to environmental changes. Hence, this dissertation seeks to further our understanding of the ice-ocean-interaction process by investigating the mechanisms causing ice shelf changes and the sensitivity of ice shelves to changes in their oceanic environment. To achieve this, a combination of observation and modeling approaches are deployed. We provide the bathymetric and subglacial discharge context for two significant ice shelves, Getz Ice Shelf in West Antarctica and West Ice Shelf in East Antarctica. Getz Ice Shelf is the largest meltwater source from Antarctica to the Southern Ocean, highlighting a need to understand what factors control its melt rate. West Ice Shelf was the least-sampled ice shelf in East Antarctica and potentially sensitive to subglacial discharge forcing. For both regions, we show in this work that subglacial discharge plays a significant role in controlling the basal melt rate. In particular, the melt rate of West Ice Shelf is primarily controlled by sub-glacial discharges. We also infer the bathymetry beneath the two ice shelves from airborne geophysical data, from which we gain first insights on the potential pathways of the Circumpolar Deep Water, which is believed to intrude into the cavity beneath the ice shelf and drive the high basal melt rates at depth. Moreover, we demonstrate the importance of accurate and high-resolution ocean bathymetry for determining modified Circumpolar Deep Water pathways and ice shelf melt rates.
Kathleen Wilson The Quaternary Sedimentology, Geomorphology, and Sediment Transport Mechanisms in The Bahamas and Turks and Caicos Islands The project, defended on April 22 2022, was supervised by Dr. David Mohrig with committee members including Drs. Travis Swanson, Timothy A Goudge, and Charles Kerans. Abstract: To be added
Eirini Poulaki Petrochronological and Structural Reconstruction of the Life Cycle of Mediterranean-Style Subduction Zones The project, defended on July 15, 2022, was supervised by Dr. Daniel Stockli with committee members including Drs. Andrew Smye, Mark P Cloos, Claudio Faccenna, and Jaime D Barnes.
. Abstract: To be added
Sean O’Donnell Origin, Dynamics, and Extent of Explosive Volcanic Eruption Hazards The project, defended on July 19, 2022, was supervised by Dr. James Gardner with committee members including Drs. Josef Dufek, Richard A Ketcham, David Mohrig, Blair Johnson, and James L Buttles.
Abstract: To be added
Cullen David Kortyna Tectonic Controls on Sediment Generation and Transfer from the Southwestern USA Laramide Hinterland to the Northwestern Gulf of Mexico The project, defended on July 20, 2022, was supervised by Dr. Daniel Stockli and Dr. Jacob Covault with committee members including Brian K Horton, Timothy F Lawton, and David Mohrig.
Abstract: To be added
Xinggang Christopher Liu Coupling between Sedimentation and Salt Deformation The project, defended on July 20, 2022, was supervised by Dr. David Mohrig with committee members including Drs. Timothy A Goudge, Tim P Dooley, Jacob A Covault, Michael R Hudec, and James L Buttles.
Abstract: To be added
Megan Flansburg Temporal Differentiation of Polyphase Ductile Fabrics in Metamorphic Core Complexes by Structurally Integrated U-PB and (U-TH)/HE Dating (Southern Cyclades, Greece and Southern Basin and Range, U.S.A.) The project, defended on July 20, 2022, was supervised by Dr. Daniel Stockli with committee members including Drs. Michael Wells, Richard A Ketcham, Sharon Mosher, and John Singleton.
Abstract: To be added
Catherine Ross Impact Crater Geo- and Thermo-Chronolocy and K-PG Boundary Deposit Provenance The project, defended on July 22, 2022, was supervised by Dr. Daniel Stockli and Dr. Sean Gulick with committee members including Drs. Natalia Artemieva, Christopher S Lowery, David Mohrig, and Timmons Erickson.
.Abstract: To be added
Kiara Gomez Jurassic Redox Conditions in the North Sea The project, defended on July 22, 2022, was supervised by Dr. Charles Kerans and Dr. Lorena Moscardelli with committee members including Drs. Swapan Sahoo, Daniel O Breecker and Toti E Larso.
Abstract: To be added
Keith Minor To be added The project, defended in July 2022, was supervised by Dr. XX with committee members including Drs. [to be added].
Abstract: To be added
Abdulah Eljalafi Anatomy of Isolated Carbonate Platforms of the Western Gulf of Mexico The project, defended on July 15, 2022, was supervised by Dr. Charles Kerans with committee members including Drs. Fernando Nunez-Useche, Rowan C. Martindale, Robert Scott, and Xavier Janson.
Abstract: To be added
Buddy Price Controls on Mixed Carbonate-Siliciclastic Slope and Basinal Depositional Architecture The project, defended in Spring 2022, was supervised by Dr. Charles Kerans and Dr. Xavier Jansen with committee members including Drs. TBD.
Abstract: To be added
Wei Xie RESERVOIR FACIES OPTIMIZATION USING HUMAN-GUIDED MACHINE LEARNING AND PROBABILISTIC ROCK-PHYSICS TEMPLATES The project, defended in Spring 2022, was supervised by Dr. Kyle Spikes with committee members including Drs. Mrinal Sen, Nicola Tisato, Sean Gulick and Zoltan Sylvester. Abstract: Reservoir facies are rock units that show distinctive features of rock types and fluid-flow properties. The characterization of reservoir facies helps us better understand the lifetime performance of reservoir. However, this process is complicated and challenging because we need to consider information from different sources with a wide range of scales (e.g., lab scale, well-log scale and seismic scale). This dissertation aims to characterize reservoir facies by integrating domain knowledge with statistical and machine learning techniques. To achieve this goal, I first present an active semi-supervised technique to classify well-log facies. This method integrates knowledge from domain experts and the data characteristics of wireline logs. It does not rely on training models or predefined classifiers. Instead, it utilizes small amounts of instructions from domain experts to guide facies clustering. The application on North Sea deep-water dataset illustrated that the semi-supervised approach could classify reservoir facies based on the data characteristics and simultaneously comply with the physical interpretations. In the second part of this dissertation, I developed a workflow using probabilistic rock-physics templates to determine the optimal reservoir facies models. Domain knowledge was imposed to build facies models with different pore-fluid parameters, and quantitative evaluations were performed to determine the optimal models. This workflow considers various types and magnitudes of errors from the petro-elastic, elastic and seismic domains. Use of Gassman equation and probabilistic rock-physics templates can mimic possible fluid types and different errors from the rock-physic modeling and well-log data. The Backus average accounts for resolution inconsistency from well-log to seismic scales. The low-frequency model building considers the possible misinterpretations of initial models in seismic inversion. I applied this workflow onto a dataset acquired in the Gulf of Mexico. Comparisons of different evaluation metrics suggested that scenario with five facies was the best facies model. Using the optimal facies models, I mapped the reservoir facies with different low-frequency models and determined areas with consistent estimations. This workflow outputs valuable information on the number and parameter distribution of facies models. It provides important guidelines for reservoir facies modeling when limited data are provided.
Compiled by Kristin Phillips, Department of Geological Sciences