02 Fakultät Bau- und Umweltingenieurwissenschaften
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/3
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Item Open Access Automated parametric Rietveld refinement and its application to two dimensional X-ray powder diffraction experiments(2011) Rajiv, Paneerselvam; Joswig, Manfred (Prof. Dr.)Parametric Rietveld refinement has opened new possibilities to simultaneously refine multiple powder diffraction patterns collected in in situ 2D experiments; in that way the models of crystallographic variables that changes with external variables can be introduced into the refinement. The substitution of a variable with its model during the refinement has several advantages, including the improved precision of variables, direct extraction/refinement of some parameters from powder data which is otherwise impractical (e.g., activation energy), etc. The basic requirement for the realization of sequential/parametric refinements (or Whole Powder Pattern Fit-WPPF) in 2D X-ray powder diffraction (XRPD) is a robust software that handles the data and performs fast WPPF. This concern has been primarily addressed in this thesis with the help of a software, in combination with the existing total pattern analysis software (Topas). The developed software could considerably speedup and automate the sequential/parametric quantitative analysis of large number of 2D powder data, which is in general a monotonous and time consuming task. The software also provides routines that automatically determines the reconstructive phase transitions of samples from the 2D powder data and facilitates the independent refinements (or WPPFs) of the determined phases. Two practical scientific applications of parametric Rietveld refinement method have been demonstrated with the assistance of the developed program. The first application concerns the kinetic analysis of several polymorphs and polymorphs-additives mixtures of copper phthalocyanine (CuPC). The reaction rate constant and the order of reactions involving the phase transitions of various forms of CuPC were directly extracted from the isothermal experimental data by introducing the Johnson-Mehl-Avrami-Kolmogorov relation as a model of the phase fraction during the multi phase parametric Rietveld refinement. Parametric refinements could be successfully performed for most of the CuPC data collected in the experiment, however the convergence of some of the refinements showed a strong dependence on the reaction rate. In many cases, the precision of the refined parameters could be improved considerably when the data collected between the optimal time steps alone were used in the refinement. The second application demonstrates the feasibility of the parameterization of crystallite size with respect to the annealing time/temperature. Some of the data samples used in the kinetic analysis (CuPC) and the temperature dependent nanocrystalline TiO2 data were used in this demonstration. The success of the parameterization of crystallite size depended strongly on the quality of the data used, on the uniformity of the variation of the crystallite size with time/temperature and also on the correctness of the model that describes the crystallite size variation with time/temperature. This application in its present form is general; as such it can be used for stabilizing other variables during parametric refinement.Item Open Access Earthquake location by distinct constraints for sparse and doubtful data(2018) Eisermann, Andreas Samuel; Joswig, Manfred (Prof. Dr. rer. nat.)Earthquakes affected mankind since the days of old, claiming more human casualties than any other nature catastrophe. The determination of the hypocenter location displays one of the key subjects of seismology. Today’s standard approaches provide for trustable location estimates - given a large amount of stations with high quality data and proper assumptions about the sub-surface velocity structure. The same approaches fail, however, when this amount of stations and quality of data is not given, e.g. in the mapping of seismically active zones using low magnitude events: Here, only few stations detect the signals that sometimes barely exceed the noise level. Seismic phases appear hence unclear, rendering the information of arrival times doubtful. Another example are real-time location schemes (e.g. in Earthquake Early Warning Systems): Here, events need to be evaluated and located within fractions of seconds without knowledge of the complete waveform, and data available only from the first few stations that already detected. The objective of this thesis lies in a methodological development that provides for more accurate single event locations in the context of sparse and doubtful data. The less data is available the more the location estimate is determined and affected by the individual datum, its uncertainties and errors. The method of choice must therefore be outlier-resistant (e.g. ignore false picks) and incorporate all parameter uncertainties. When data is few, solutions may further be ambiguous (not due to errors in the input parameters, being exact solutions to a set of even ideal arrival times), meaningly: Multiple, significantly separated location candidates may exist. Also, models are usually only rough and simplified representations of the subsurface structure and will often not explain the observed data well enough. Today's standard approaches often disregard the corresponding uncertainties and, hence, often displace the hypocenter significantly to the true location - outside of the assumed error margins. Mislocations in earthquake early warning or forensic seismology may have far-reaching implications for society and on the political level. This thesis provides therefore a novel location methodology that incorporates the important uncertainties, naturally disregards outliers and thereby leads to robust hypocenter estimates. The work presented builds on the concept of (graphical) jackknifing, which contrary to most of today's standard approaches doesn't attempt to minimize the error in the over-determined system directly, but decomposes the system first into exactly- or even under-determined subsystems. This results in distinct location constraints that are based on arrival time differences between two- or three phases, only. Each constraint identifies a subset of space as possible hypocenter region. The combination of multiple constraints consecutively constrains the final hypocenter region. Since a single constraint relies on a minimum amount of phase onsets, only, solution discrepancies can be traced back to the individual phase data, which allows the data base (e.g. outliers) to be re-evaluated. The global solution is finally recomposed based on the sub-solutions deemed trustworthy, which provides robust and outlier resistant solutions. This concept is built on, supporting for three dimensional station layouts, complex velocity models and a volumetric computation, which render this approach suitable for a wide class of modern applications. A real-time methodology that regards uncertainties in phase picks, phase types and model parameters provides for robust and accurate locations when data is uncertain and sparse. New constraints are introduced, which allow to resolve ambiguities and provide for faster hypocenter and magnitude estimates in Earthquake Early Warning. A new direct search scheme is developed that integrates constraint probabilities over grid cells, which ensures the identification of sharp hypocenter regions independent of the grid's resolution, satisfying the demand for a complete search. The improvement in location quality is demonstrated using several examples ranging from gas-field low-magnitude event monitoring, forensic seismology to examples of real-time locations in Earthquake Early Warning.