Seventeen electromagnetic transient soundings were done on the lower east flank of Kilauea volcano, Hawaii. Each sounding is based on the response or the earth as a function of time to a step function of current in a horizontal linear source. Interpretation of these response measurements is usually done by matching the data to standard model curves or asymptotic expressions; however, these methods presuppose that each datum has been measured with a relative precision (e.g. a precision of 5%) whereas, sounding, each datum is commonly measured with an absolute precision (e.g. a precision of 10µv). Therefore, a general inversion technique based on linear comparisons between the data and model values was used for the interpretations of the data in this study. The resulting geoelectric model shows that the structure is uniform vertically to a depth of 1000 m below sea level. There are broad, but distinct, lateral variations in the interpreted conductivity values ranging from 0.10 to 0.16 -mho/m in most of east Puna to anomalous values of 0.30 to 0.50 mho/m in a particular area south of the rift at Puu Honuaula (see Figure 7). Based on these conductivity estimates, groundwater temperatures in the anomalous area are not expected to exceed 1500 C to depths of 1000 m below sea level. / ill / maps

Thesis (M. Eng.), Memorial University of Newfoundland, 1998. / Bibliography: leaves 74-76.

Thesis (Ocean E)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1979. / Supervised by Ole Secher Madsen and William D. Grant. Includes bibliographical references (leaves 142-144).

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Dans cette thèse, une comparaison entre différentes topologies des machines a été tout d'abord réalisée. Deux machines électriques: Machine à Réluctance Variable (MRV) et Machine à Commutation de Flux (MCF) sont ensuite choisies pour les études suivantes. Un modèle électromagnétique-thermiquement couplé est effectué pour ces deux structures. Ce modèle est basé sur une caractérisation préalable par la méthode des éléments finis (EF) 2D via le calcul du couple instantané, des deux composantes de l'induction magnétique (Br et B&#952-) de chaque élément du maillage du rotor ainsi que du stator pour différentes densités des courantes efficaces et différentes positions du rotor. Ces résultats sont ensuite utilisés dans les modèles analytiques de pertes Joule et de pertes fer permettant de calculer les pertes Joule et les pertes fer sur un cycle de fonctionnement. Des modèles thermiques transitoires tels que le réseau de résistances thermiques et l’EF 2D sont effectués, dans lesquels les pertes instantanées obtenues précédemment sont utilisées comme sources de chaleur pour le calcul des variations des températures dans différents composants d’une machine électrique sur cycle de fonctionnement. Un modèle défaut-thermique d’une MCF triphasée est aussi mis en ouvre. Les défauts étudiés dans cette thèse sont principalement dus au court-circuit: le court-circuit entre-spires d’une phase, le court-circuit entre-spires et entre-phase dans une MCF sans redondance, le court-circuit d’une phase ou de trois phases dans une MCF avec redondance. A l’aide de MATLAB/Simulink, les courants sains et les courants de court-circuit sont obtenus, et les pertes instantanées peuvent être calculées en utilisant la méthode mentionnée précédemment. Par conséquent, le comportement thermique en cas de défauts peut être prévu. Enfin, des études de défauts pour une MCF hexa-phasée sans redondance sont effectuées, et un convertisseur du type pont complet est utilisé pour alimenter la machine. Cela nous permet de contrôler indépendamment chaque phase en cas de défauts. Les défauts sont entre autres le circuit ouvert ou le court-circuit dans une ou plusieurs phases (jusqu'à trois). Certaines méthodes de correction telles que: l'augmentation de l’amplitude du courant dans les phases saines et / ou modifier leurs phases, sont proposées pour maintenir le couple électromagnétique tout en minimisant l'ondulation de couple. Les résultats analytiques et numériques ont montré la bonne efficacité des méthodes proposées tant en cas de l’ouverture des phases qu’en cas du court-circuit des phases. / In this thesis, a comparison among different machine topologies has been firstly realized, and two electrical machines: Switched Reluctance Motors (SRMs) and Flux-Switching Permanent Magnet (FSPM) motors are then chosen for the following studies. A fast and precise coupled electromagnetic-thermal model is performed for these two structures. This model is based on a prior steady characterization by Finite Element method (FEM) 2D via calculating the instantaneous torque, the two components of magnetic induction (B_r and B_&#952-) of each element of rotor as well as stator for different RMS current densities and different rotor positions. These results are then used in the analytical copper and iron losses models for calculating the instantaneous copper and rotor as well as stator iron losses during one driving cycle. The Lumped Parameter (LP) and FEM 2D transient thermal models are then carried out, in which the previously obtained instantaneous power losses are used as heat sources for calculating the temperatures of different motor parts during driving cycles. A faulty-thermal analysis for a three-phase FSPM motor is also achieved. The faults in this thesis are mainly due to short-circuit (SC), such as inter-turn SC in phases or inter-turn and inter-phase SC, one phase or three phases SC in a redundant FSPM motor. Based on MATLAB-Simulink, the faulty information as the normal and short-circuit currents can be obtained, the power losses can then be calculated as previously. Thus, the thermal behavior of the machines can be predicted under faulty mode. The coupled Thermal-Electromagnetic Analysis method in this thesis can also be extended for all the other applications with driving cycles. Finally, the faulty analysis for a six-phase FSPM motor is performed, and one six-phase full bridge inverter is applied to drive the machine. This allows us to control each phase independently under faulty mode. The faults here are open-circuit or short-circuit in one or several phases (up to three). Some correction methods such as: increasing healthy current and/or change their phase angles, are proposed to maintain the electromagnetic torque while minimizing the torque ripple. The analytical and FEM 2D results have shown the good efficiency of the proposed methods both in case of phase open-circuit and in case of phase short-circuit.

Various error estimation and adaptive refinement techniques for the solution of electromagnetic integral equations were developed. Residual based error estimators and h-refinement implementations were done for the Method of Moments (MoM) solution of electromagnetic integral equations for a number of different problems. Due to high computational cost associated with the MoM, a cheaper solution technique known as the Locally-Corrected Nyström (LCN) method was explored. Several explicit and implicit techniques for error estimation in the LCN solution of electromagnetic integral equations were proposed and implemented for different geometries to successfully identify high-error regions. A simple p-refinement algorithm was developed and implemented for a number of prototype problems using the proposed estimators. Numerical error was found to significantly reduce in the high-error regions after the refinement. A simple computational cost analysis was also presented for the proposed error estimation schemes. Various cost-accuracy trade-offs and problem-specific limitations of different techniques for error estimation were discussed. Finally, a very important problem of slope-mismatch in the global error rates of the solution and the residual was identified. A few methods to compensate for that mismatch using scale factors based on matrix norms were developed.

Thesis (M.Sc.)--University of Adelaide, Dept. of Geology and Geophysics, 2003. / Includes bibliographical references (leaves 112-115).

Recent advances in computational power, as well as the hard work of a handful of brilliant scientists, have made Bayesian inversion of geophysical observations possible. This development is highly significant, as it permits the quantification of uncertainty, not only on the inverted model parameters, but also on related properties of interest. This dissertation focuses on the application of a particular kind of Bayesian inversion – trans-dimensional Markov chain Monte Carlo – to electromagnetic data, specifically airborne transient electromagnetic, magnetotelluric, and surface-towed controlled source electromagnetic data. In chapters 2-4, these data, both real and synthetic, are inverted for 1D models of subsurface electrical resistivity. In chapter 5, magnetotelluric data are inverted for 2D models of resistivity – the first time, to the best of my knowledge, that trans-dimensional Bayesian inversion of magnetotelluric data for 2D models has been achieved. In each instance, the uncertainty on bulk resistivity provided by the Bayesian inversion is used to estimate uncertainty on related subsurface properties, including pore fluid resistivity and salinity, porosity, melt fraction, melt volatile content, and bulk mantle volatile inventory. Chapter 1 introduces the topic of Bayesian inversion of electromagnetic data. Chapter 2 concerns trans-dimensional Bayesian inversion of airborne transient electromagnetic data. These data were collected above Taylor Glacier in the McMurdo Dry Valleys region of Antarctica in 2011, and were inverted using deterministic inverse methods to image a conductive channel beneath the glacier, interpreted as a package of brine-saturated sediments. The Bayesian inversion of these data confirms the existence of a conductive channel and provides quantitative uncertainties on the resistivity as a function of depth. These uncertainties are used in conjunction with Archie’s Law to estimate uncertainty on the resistivity of the pore fluids in the sediments. Additionally, the Kullback-Leibler divergence – a statistical measure of the dissimilarity of two distributions – is introduced as a measure of how much influence the observations have on the model parameters as a function of depth. The utility of Bayesian inversion in estimating the noise floor necessary to effectively resolve model structure is demonstrated. In chapter 3, a joint Bayesian framework for inverting electromagnetic data is introduced. A modified version of the algorithm utilized in chapter 2 is applied to jointly invert marine magnetotelluric and surface-towed controlled source electromagnetic data. These data were collected offshore New Jersey in 2015 to image a freshwater aquifer in the continental shelf. Deterministic inversions of this data clearly image a resistive body at depths consistent with low salinity from bore hole measurements collocated with the electromagnetic survey. The Bayesian inversion of this data set again confirms the existence of the resistive region while further providing uncertainty on the inverted resistivity with depth. In some instances, bimodality in the posterior distribution is found, demonstrating the importance of Bayesian inverse methods for fully exploring the model space. The uncertainty on bulk resistivity is used in conjunction with Archie’s Law and the porosity from bore hole measurements in a Monte Carlo framework to estimate uncertainty in the salinity of the pore water as a function of depth for three well locations. These estimates match well with measured salinities at these locations, validating the use of the Bayesian posterior in the context of a Monte Carlo framework to estimate uncertainty on related physical properties. In chapter 4, seafloor magnetotelluric data are again inverted for 1D models of subsurface resistivity, this time to image a conductive channel at the base of the lithosphere. The data are a subset of a deployment of 50 Broadband MT instruments on the seafloor above the Cocos plate offshore Nicaragua. Deterministic inversions of this data revealed a conductive structure at 45-70 km depth, beneath the Cocos plate. This earlier analysis concluded that melt was required at the lithosphere-asthenosphere boundary (LAB) to explain the inverted resistivity, but the deterministic inverse tools available at the time did not permit quantitative uncertainties – on the conductive anomaly itself, the requirement for partial melt, the degree of partial melt, or the degree of mantle hydration. Bayesian inversion of data from two magnetotelluric sites confirm that the conductor is indeed robust, and that melt is required by nearly 100% of the models that fit the data. Further, the resistivity uncertainty from the Bayesian inversion is used in conjunction with petrological modeling of partial melting in the mantle and an estimated probability distribution for temperature to place constraints on the degree of partial melt and mantle volatile (water and carbon) inventory over the depth range 45-63 km. This analysis concludes that large melt fractions and either high temperatures or a high degree of mantle hydration are likely needed to explain the resistivities produced by the Bayesian inversion, potentially explaining the mechanism for plate sliding that enables plate tectonics. Finally, chapter 5 introduces 2D trans-dimensional Bayesian inversion of magnetotelluric data, for the first time to my knowledge. A Gaussian Process-parametrized, trans-dimensional Markov chain Monte Carlo algorithm is used with MARE2DEM to invert synthetic data as well as field data from the Gemini data set from the Gulf of Mexico. For Bayesian inversion to be computationally feasible beyond inverting for 1D models, the cost of forward modeling must be reduced, as well as the number of model parameters that the algorithm must sample over. The first challenge is addressed through high performance computing. The forward modeling is performed on a cluster. In addition, we implement parallel tempering, where multiple Markov chains are run in parallel and swap models at each iteration, vastly increasing the rate at which the model space is explored and sampled. The curse of dimensionality is addressed by utilizing a Machine Learning technique known as a Gaussian Process to represent the model with far fewer parameters than required in a typical discrete finite difference or finite element representation of the subsurface. The Bayesian inversion of the Gemini data successfully recovers the model structure obtained by deterministic inversion of the same data, but additionally provides uncertainty on bulk resistivity. This thesis demonstrates the power and utility of Bayesian inversion to move beyond single estimates of subsurface resistivity. Not only does the work in this dissertation show that Bayesian inversion can provide uncertainty on inverted resistivity, it shows that these inverted uncertainties can be used to place quantitative constraints on parameters related to bulk resistivity. This is crucial to rendering the information obtained from inversion of electromagnetic data useful to disciplines far beyond electromagnetic geophysics.

Includes bibliographical references (leaves 112-115) Towed DC Resistivity and Transient Electromagnetic arrays have been trialled for suitability in monitoring salt-loads on the Murray River at Waikerie, South Australia.