Browsing by Author "Martin, Eileen R."

Now showing 1 - 12 of 12
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    Computational Advancements for Solving Large-scale Inverse Problems
    Cho, Taewon (Virginia Tech, 2021-06-10)
    For many scientific applications, inverse problems have played a key role in solving important problems by enabling researchers to estimate desired parameters of a system from observed measurements. For example, large-scale inverse problems arise in many global problems and medical imaging problems such as greenhouse gas tracking and computational tomography reconstruction. This dissertation describes advancements in computational tools for solving large-scale inverse problems and for uncertainty quantification. Oftentimes, inverse problems are ill-posed and large-scale. Iterative projection methods have dramatically reduced the computational costs of solving large-scale inverse problems, and regularization methods have been critical in obtaining stable estimations by applying prior information of unknowns via Bayesian inference. However, by combining iterative projection methods and variational regularization methods, hybrid projection approaches, in particular generalized hybrid methods, create a powerful framework that can maximize the benefits of each method. In this dissertation, we describe various advancements and extensions of hybrid projection methods that we developed to address three recent open problems. First, we develop hybrid projection methods that incorporate mixed Gaussian priors, where we seek more sophisticated estimations where the unknowns can be treated as random variables from a mixture of distributions. Second, we describe hybrid projection methods for mean estimation in a hierarchical Bayesian approach. By including more than one prior covariance matrix (e.g., mixed Gaussian priors) or estimating unknowns and hyper-parameters simultaneously (e.g., hierarchical Gaussian priors), we show that better estimations can be obtained. Third, we develop computational tools for a respirometry system that incorporate various regularization methods for both linear and nonlinear respirometry inversions. For the nonlinear systems, blind deconvolution methods are developed and prior knowledge of nonlinear parameters are used to reduce the dimension of the nonlinear systems. Simulated and real-data experiments of the respirometry problems are provided. This dissertation provides advanced tools for computational inversion and uncertainty quantification.
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    Efficient Algorithms for Data Analytics in Geophysical Imaging
    Kump, Joseph Lee (Virginia Tech, 2021-06-14)
    Modern sensing systems such as distributed acoustic sensing (DAS) can produce massive quantities of geophysical data, often in remote locations. This presents significant challenges with regards to data storage and performing efficient analysis. To address this, we have designed and implemented efficient algorithms for two commonly utilized techniques in geophysical imaging: cross-correlations, and multichannel analysis of surface waves (MASW). Our cross-correlation algorithms operate directly in the wavelet domain on compressed data without requiring a reconstruction of the original signal, reducing memory costs and improving scalabiliy. Meanwhile, our MASW implementations make use of MPI parallelism and GPUs, and present a novel problem for the GPU.
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    Fiber-optic Seismology
    Lindsey, Nathaniel J.; Martin, Eileen R. (2021)
    Distributed Acoustic Sensing (DAS) is an emerging technology that repurposes a fiber-optic cable as a dense array of strain sensors. This technology repeatedly pings a fiber with laser pulses, measuring optical phase changes in Rayleigh backscattered light. DAS is beneficial for studies of fine-scale processes over multi-kilometer distances, long-term time-lapse monitoring, and deployment in logistically challenging areas (e.g. high temperatures, power limitations, land access barriers). These bene fits have motivated a decade of applications in subsurface imaging and microseismicity monitoring for energy production and carbon sequestration. DAS arrays have recorded microearthquakes, regional earthquakes, teleseisms, and infrastructure signals. Analysis of these wavefields is enabling earthquake seismology where traditional sensors were sparse, as well as structural and near-surface seismology. These studies improved understanding of DAS instrument response through comparison with traditional seismometers. More recently DAS has been used to study cryosphere systems, marine geophysics, geodesy and volcanology. Further advancement of geoscience using DAS requires several community efforts related to instrument access, training, outreach and cyberinfrastructure.
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    Fully Distributed Multi-Material Magnetic Sensing Structures for Multiparameter DAS Applications
    Hileman, Zachary Daniel (Virginia Tech, 2022-06-29)
    This dissertation demonstrates the first of its kind distributed magnetic field sensor based on a fiber optic distributed acoustic sensing (DAS) scheme. Ferromagnetic nickel and Metglas® were dispersed internally within a fiber optic preform and then drawn on an in-house fiber optic draw tower to lengths in the kilometers. Due to the close proximity of the ferromagnetic metals and fiber optic core, the magnetostrictive strain response of the ferromagnetic materials when exposed to a magnetic field would perturbate within the fiber cladding and transfer that strain, internally, to the fiber optic core. Strain resulting from the magnetostrictive effect allows the DAS based sensor to accurately translate strain into readable magnetic field data. Due to the high sensitivity seen in this sensor design, multiparameter sources, acoustic and magnetic fields, were tested and validated and a three dimensional magnetic-field vector sensor was proposed. Numerical analysis of the novel sensor design was first implemented using COMSOL Multiphysics, where inputs such as magnetostrictive element shape, size, distance, and number were first investigated. Upon optimizing system constraints, the sensor design was further modified such that single mode operation was consistent across multiple fiber draws while retaining high strain transfer from the ferromagnetic elements to the fiber optic core. Ferromagnetic material selection was evaluated as a function of the saturation magnetostriction constants and a total of 4 modules were used to fully characterize the complex physics involved in this sensor design. All fabrication and testing were performed in-house using a full scale 3-story fiber draw tower and custom environmental testing stations to imitate naturally occurring events such as magnetic or acoustic point sources. A unique stacking method was used to embed ferromagnetic nickel and Metglas® into a fiber optic preform which when combined with a custom fiber draw process resulted in consistent multi-material fibers drawn to lengths of 1-km. In-house testing facilities included different types of electromagnetic generators, in addition to a soil test bed, and an outdoor test bed which allowed 100 meters of fiber to be tested simultaneously. All tested sensors demonstrated high strain transfer capabilities on the order of 0.01-10 μϵ depending on the materials used, ferromagnetic rod number, and core to metal spacing. Due to the sensitivity of the system the difference between AC and DC was distinct, and directional magnetostriction was studied. Transverse and longitudinal magnetic wave propagation was controlled through a solenoid and rectangular Helmholtz coil, both built in-house. A three-dimensional magnetic field vector sensor was proposed due to the success of the magnetic field sensor, and a design was proposed and initially tested to validate direction as a function of field strength and distance. To summarize, this dissertation explores the first fully distributed magnetic field sensor using DAS based techniques and one of the first multi-material fiber draw processes which can produce consistent single mode fiber up to 1-km. Due to extensive FEA modeling, multiple iterations of the magnetic sensor were fully characterized and an equation describing the relationship between sensor design and strain transfer has been created and validated experimentally. Multi-parameter tests including acoustic and magnetic fields were implemented and an algorithm was developed to separate the mixed signals. Finally, a test was performed to demonstrate the feasibility of sensing magnetic fields directionally. Cumulative results demonstrate a high-quality sensor alternative to current designs which may surpass other magnetic sensors due to innate multi-parameter capabilities, in addition to the inexpensive production cost and extremely long operating lengths.
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    Improving CPT-Based Earthquake Liquefaction Hazard Assessment at Challenging Soil Sites
    Yost, Kaleigh McLaughlin (Virginia Tech, 2022-11-15)
    Earthquake-induced soil liquefaction is a phenomenon in which saturated, sandy soil loses its strength and stiffness during earthquake shaking. Liquefaction can be extremely costly and damaging to infrastructure. The commonly used "simplified" stress-based liquefaction triggering framework is correlated with metrics computed from in-situ tests like the Cone Penetration Test (CPT). While CPT-based procedures have been shown to accurately predict liquefaction occurrence in homogenous, sandy soil profiles, they tend to over-predict the occurrence of liquefaction in challenging, highly interlayered soil profiles. One contributing factor to the over-prediction is multiple thin-layer effects in CPT data, a phenomenon in which data in interlayered zones is blurred or averaged, making it difficult to identify specific layer boundaries and associated CPT parameters like tip resistance. Multiple thin-layer correction procedures have been proposed to convert the measured tip resistance in an interlayered profile (qm) to the "true" or characteristic tip resistance (qt) that would be measured without the influence of multiple thin-layer effects. In this dissertation, the efficacy of existing multiple thin-layer correction procedures is assessed. It is shown that existing procedures are not effective for layer thicknesses equal to or less than about 1.6 times the diameter of the cone. Two new multiple thin-layer correction procedures are proposed. Furthermore, a framework for numerically simulating CPTs in interlayered soil profiles using the Material Point Method (MPM) is developed. A framework for linking uncertainties associated with the numerical analyses and the laboratory CPT calibration chamber tests used to calibrate the numerical analyses is also proposed. Finally, a database of laboratory and numerically-generated CPT data is presented. It is shown how this database can be used to improve existing, and develop new, multiple thin-layer correction procedures. Ultimately, the work detailed in this dissertation will improve the characterization of highly interlayered soil profiles using CPTs to support more accurate liquefaction hazard assessment at challenging soil sites.
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    Improving Separation of Signals from Multiple Physical Quantities Detected by Sensor Arrays
    Morgan, Sarah Elizabeth (Virginia Tech, 2022-05-31)
    Modern array sensing systems, such as distributed fiber optic sensing, are used in many applications which may record a mixture of responses to multiple physical quantities. In these applications, it may be helpful to be able to separate this mixture of responses into the signals resulting from the individual sources. This is similar to the cocktail party problem posed with Independent Component Analysis (ICA), in which we use gradient ascent and fixed point iteration optimization algorithms to achieve this separation. We then seek to apply the problem setup from ICA to mixed signals resulting from a sensor array with the goal of maintaining coherence throughout resulting spatial arrays. We propose a new post-processing technique after separation to pair up the signals from different types of physical quantities based on the Symmetric Reverse Cuthill-McKee (SRCM) and Symmetric Approximate Minimum Degree (SAMD) permutations of the coherence matrix.
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    A Linear Algorithm for Ambient Seismic Noise Double Beamforming Without Crosscorrelations
    Martin, Eileen R. (2020-01-02)
    Geoscientists and engineers are increasingly using denser arrays for continuous seismic monitoring, and often turning to ambient seismic noise interferometry for low-cost near-surface imaging. While ambient noise interferometry greatly reduces acquisition costs, the computational cost of pair-wise comparisons between all sensors can be prohibitively slow or expensive for applications in engineering and environmental geophysics. Double beamforming of noise correlation functions is a powerful technique to extract body waves from ambient noise, but it is typically performed via pair-wise comparisons between all sensors in two dense array patches (scaling as the product of the number of sensors in one patch with the number of sensors in the other patch). By rearranging the operations involved in the double beamforming transform, we propose a new algorithm that scales as the sum of the number of sensors in two array patches. Compared to traditional double beamforming of noise correlation functions, the new method is more scalable, easily parallelized, and does not require raw data to be exchanged between dense array patches.
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    Multidimensional and High Frequency Heat Flux Reconstruction Applied to Hypersonic Transitional Flows
    Nguyen, Nhat Minh (Virginia Tech, 2023-09-12)
    The ability to predict and control laminar-to-turbulent transition in high-speed flow has a substantial effect on heat transfer and skin friction, thus improving the design and operation of hypersonic vehicles. The control of transition on blunt bodies is essential to improve the performance of lifting and control surfaces. The objective of this Ph.D. research is to develop efficient and accurate algorithms for the detection of high-frequency heat flux fluctuations supported by hypersonic flow in transitional boundary layers. The focus of this research is on understanding the mathematical properties of the reconstruction such as regularity, sensitivity to noise, multi-resolution, and accuracy. This research is part of an effort to develop small-footprint heat flux sensors able to measure high-frequency fluctuations on test articles in a hypersonic wind tunnel with a small curvature radius. In the present theoretical/numerical study a multi-resolution formulation for the direct and inverse reconstruction of the heat flux from temperature sensors distributed over a multidimensional solid in a hypersonic flow was developed and validated. The solution method determines the thermal response by approximating the system Green's function with the Galerkin method and optimizes the heat flux distribution by fitting the distributed surface temperature data. Coating and glue layers are treated as separate domains for which the Green's function is obtained independently. Connection conditions for the system Green's function are derived by imposing continuity of heat flux and temperature concurrently at all interfaces. The solution heat flux is decomposed on a space-time basis with the temporal basis a multi-resolution wavelet with arbitrary scaling function. Quadrature formulas for the convolution of wavelets and the Green's function, a reconstruction approach based on isoparametric mapping of three-dimensional geometries, and a boundary wavelet approach for inverse problems were developed and verified. This approach was validated against turbulent conjugate heat transfer simulations at Mach 6 on a blunted wedge at 0 angle of attack and wind tunnel experiments of round impinging jet at Mach 0.7 It was found that multidimensional effects were important near the wedge shoulder in the short time scale, that the L-curve regularization needed to be locally corrected to analyze transitional flows and that proper regularization led to sub-cell resolution of the inverse problem. While the L2 regularization techniques are accurate they are also computationally inefficient and lack mathematical rigor. Optimal non-linear estimators were researched both as means to promote sparsity in the regularization and to pre-threshold the inverse heat conduction problem. A novel class of nonlinear estimators is presented and validated against wind tunnel experiments for a flat-faced cylinder also at Mach 6. The new approach to hypersonic heat flux reconstruction from discrete temperature data developed in this thesis is more efficient and accurate than existing techniques.
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    Radar Imaging Applications for Mining and Landmine Detection
    Abbasi Baghbadorani, Amin (Virginia Tech, 2022-08-02)
    The theme of this dissertation is to advance safety hazard mitigation by detecting and characterizing hidden targets of concern. Ground-penetrating radar (GPR) is used to detect and characterize hidden targets that pose safety hazards at Earth's surface, within shallow soil, and within rock. The resulting images detect unexploded ordnance (UXO) and detect fractures that pose collapse hazards in a mine. Detecting and characterizing fractures and voids within rock prior to excavation can enable mitigation of mine collapse hazards. GPR data were acquired on the wall of a pillar in an underground mine. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Pillar wall roughness was included in migration, a wavefield imaging algorithm, to quantitatively locate fractures and voids and map their spatial relationships within the rock. Quantifying the radar reflection amplitudes enabled mapping the distance between fracture walls. Detecting and characterizing UXO and landmines from a safe distance can enable de-mining. Migration was used to improve GPR imaging for unmanned aerial vehicle (UAV) data acquisitions. Existing algorithms were adapted for UAV flight irregularities and surface topography, and a new algorithm was developed that does not depend on the unknown soil wavespeed. Errors associated with wavespeed and raypath assumptions were quantified. The algorithms were tested with real and synthetic datasets. The improved and new algorithms are more successful than previous algorithms. To detect linear targets at all orientations, fully polarized GPR data are needed. Polarity combinations were investigated to optimize the detection of surface and subsurface small targets and linear targets. Scattering caused by topographic roughness is the primary shallow subsurface noise. For subsurface targets, detection is optimized by migration plus a polarity combination that captures all scattered energy. Strong reflection and scattering from the air-ground boundary can hide surface targets. Detection is optimized by removing the strong isotropic surface scattering, imaging targets by their anisotropic scattering.
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    Robust identification and characterization of thin soil layers in cone penetration data by piecewise layer optimization
    Cooper, Jon; Martin, Eileen R.; Yost, Kaleigh; Yerro Colom, Alba; Green, Russell (Elsevier, 2021-08-11)
    Cone penetration testing (CPT) is a preferred method for characterizing soil profiles for evaluating seismic liquefaction triggering potential. However, CPT has limitations in characterizing highly stratified profiles because the measured tip resistance ($q_c$) of the cone penetrometer is influenced by the properties of the soils above and below the tip. This results in measured $q_c$ values that appear ``blurred" at sediment layer boundaries, inhibiting our ability to characterize thinly layered strata that are potentially liquefiable. Removing this ``blur" has been previously posed as a continuous optimization problem, but in some cases this methodology has been less efficacious than desired. Thus, we propose a new approach to determine the corrected $q_c$ values (i.e. values that would be measured in a stratum absent of thin-layer effects) from measured values. This new numerical optimization algorithm searches for soil profiles with a finite number of layers which can automatically be added or removed as needed. This algorithm is provided as open-source MATLAB software. It yields corrected $q_c$ values when applied to computer-simulated and calibration chamber CPT data. We compare two versions of the new algorithm that numerically optimize different functions, one of which uses a logarithm to refine fine-scale details, but which requires longer calculation times to yield improved corrected $q_c$ profiles.
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    Sensing Earth and environment dynamics by telecommunication fiber-optic sensors: an urban experiment in Pennsylvania, USA
    Zhu, Tieyuan; Shen, Junzhu; Martin, Eileen R. (Copernicus Publications, 2021-01-28)
    Continuous seismic monitoring of the Earth’s near surface (top 100 m), especially with improved resolution and extent of data both in space and time, would yield more accurate insights about the effect of extreme-weather events (e.g., flooding or drought) and climate change on the Earth’s surface and subsurface systems. However, continuous long-term seismic monitoring, especially in urban areas, remains challenging. We describe the Fiber Optic foR Environmental SEnsEing (FORESEE) project in Pennsylvania, USA, the first continuous-monitoring distributed acoustic sensing (DAS) fiber array in the eastern USA. This array is made up of nearly 5 km of pre-existing dark telecommunication fiber underneath the Pennsylvania State University campus. A major thrust of this experiment is the study of urban geohazard and hydrological systems through near-surface seismic monitoring. Here we detail the FORESEE experiment deployment and instrument calibration, and describe multiple observations of seismic sources in the first year. We calibrate the array by comparison to earthquake data from a nearby seismometer and to active-source geophone data. We observed a wide variety of seismic signatures in our DAS recordings: natural events (earthquakes and thunderstorms) and anthropogenic events (mining blasts, vehicles, music concerts and walking steps). Preliminary analysis of these signals suggests DAS has the capability to sense broadband vibrations and discriminate between seismic signatures of different quakes and anthropogenic sources. With the success of collecting 1 year of continuous DAS recordings, we conclude that DAS along with telecommunication fiber will potentially serve the purpose of continuous near-surface seismic monitoring in populated areas.
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    Utilizing Recurrent Neural Networks for Temporal Data Generation and Prediction
    Nguyen, Thaovy Tuong (Virginia Tech, 2021-06-15)
    The Falling Creek Reservoir (FCR) in Roanoke is monitored for water quality and other key measurements to distribute clean and safe water to the community. Forecasting these measurements is critical for management of the FCR. However, current techniques are limited by inherent Gaussian linearity assumptions. Since the dynamics of the ecosystem may be non-linear, we propose neural network-based schemes for forecasting. We create the LatentGAN architecture by extending the recurrent neural network-based ProbCast and autoencoder forecasting architectures to produce multiple forecasts for a single time series. Suites of forecasts allow for calculation of confidence intervals for long-term prediction. This work analyzes and compares LatentGAN's accuracy for two case studies with state-of-the-art neural network forecasting methods. LatentGAN performs similarly with these methods and exhibits promising recursive results.