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Least-squares Migration and Full Waveform Inversion with Multisource Frequency SelectionHuang, Yunsong 09 1900 (has links)
Multisource Least-Squares Migration (LSM) of phase-encoded supergathers has shown great promise in reducing the computational cost of conventional migration. But for the marine acquisition geometry this approach faces the challenge of erroneous misfit due to the mismatch between the limited number of live traces/shot recorded in the field and the pervasive number of traces generated by the finite-difference modeling method. To tackle this mismatch problem, I present a frequency selection strategy with LSM of supergathers. The key idea is, at each LSM iteration, to assign a unique frequency band to each shot gather, so that the spectral overlap among those shots—and therefore their crosstallk—is zero. Consequently, each receiver can unambiguously identify and then discount the superfluous sources—those that are not associated with the receiver in marine acquisition. To compare with standard migration, I apply the proposed method to 2D SEG/EAGE salt model and obtain better resolved images computed at about 1/8 the cost; results for 3D SEG/EAGE salt model, with Ocean Bottom Seismometer (OBS) survey, show a speedup of 40×.
This strategy is next extended to multisource Full Waveform Inversion (FWI) of supergathers for marine streamer data, with the same advantages of computational efficiency and storage savings. In the Finite-Difference Time-Domain (FDTD) method, to mitigate spectral leakage due to delayed onsets of sine waves detected at receivers, I double the simulation time and retain only the second half of the simulated records. To compare with standard FWI, I apply the proposed method to 2D velocity model of SEG/EAGE salt and to Gulf Of Mexico (GOM) field data, and obtain a speedup of about 4× and 8×.
Formulas are then derived for the resolution limits of various constituent wavepaths pertaining to FWI: diving waves, primary reflections, diffractions, and multiple reflections. They suggest that inverting multiples can provide some low and intermediate-wavenumber components of the velocity model not available in the primaries. In addition, diffractions can provide twice or better the resolution as specular reflections for comparable depths of the reflector and diffractor. The width of the diffraction-transmission wavepath is on the order of λ at the diffractor location for the diffraction-transmission wavepath.
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Public Opinion on Tobacco, Alcohol, and Sugar Policy and its Economic Implications in Sweden : A study on sociodemographic factors’ effects on health policy attitudes of SwedesKarlsson, Jonas January 2020 (has links)
Using paired samples t-tests, this study examines attitudes toward government intervention to decrease the consumption of tobacco, alcohol, and sugar to improve public health in Sweden. The effects of the four sociodemographic variables gender, age, education, and income on attitudes toward health policies are tested using Ordinary Least Squares and ordered probit regressions. The research is performed using cross-sectional data which is supplied by a national survey. The results show that tobacco should be regulated the most, followed by alcohol and lastly sugar. According to the respondents, tobacco and alcohol consumption need clear societal restrictions while individuals should be responsible for their sugar consumption. This implies that tobacco and alcohol restrictions introduced by the government should be effective and should, therefore, reduce the consumption and subsequently decrease a country’s economic costs. The opposite is true for sugar policy. Women, younger people, highly educated people, and people with higher incomes are positively related to support toward tobacco restrictions. Women, younger people, and highly educated people show more support for alcohol restrictions. Lastly, respondents with higher levels of education are more supportive of sugar restrictions.
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Using Second-Order Information in Training Deep Neural NetworksRen, Yi January 2022 (has links)
In this dissertation, we are concerned with the advancement of optimization algorithms for training deep learning models, and in particular about practical second-order methods that take into account the structure of deep neural networks (DNNs). Although first-order methods such as stochastic gradient descent have long been the predominant optimization algorithm used in deep learning, second-order methods are of interest because of their ability to use curvature information to accelerate the optimization process.
After the presentation of some background information in Chapter 1, Chapters 2 and 3 focus on the development of practical quasi-Newton methods for training DNNs. We analyze the Kronecker-factored structure of the Hessian matrix of multi-layer perceptrons and convolutional neural networks and consequently propose block-diagonal Kronecker-factored quasi-Newton methods named K-BFGS and K-BFGS(L). To handle the non-convexity nature of DNNs, we also establish new double damping techniques for our proposed methods. Our K-BFGS and K-BFGS(L) methods have memory requirements comparable to first-order methods and experience only mild overhead in terms of per-iteration time complexity.
In Chapter 4, we develop a new approximate natural gradient method named Tensor Normal Training (TNT), in which the Fisher matrix is viewed as the covariance matrix of a tensor normal distribution (a generalized form of the normal distribution). The tractable Kronecker-factored approximation to the Fisher information matrix that results from this approximation enables TNT to enjoy memory requirements and per-iteration computational costs that are only slightly higher than those for first-order methods. Notably, unlike KFAC and K-BFGS/K-BFGS(L), TNT only requires the knowledge of the shape of the trainable parameters of a model and does not depend on the specific model architecture.
In Chapter 5, we consider the subsampled versions of Gauss-Newton and natural gradient methods applied to DNNs. Because of the low-rank nature of the subsampled matrices, we make use of the Sherman-Morrison-Woodbury formula along with backpropagation to efficiently compute their inverse. We also show that, under rather mild conditions, the algorithm converges to a stationary point if Levenberg-Marquardt damping is used.
The results of a substantial number of numerical experiments are reported in Chapters 2, 3, 4 and 5, in which we compare the performance of our methods to state-of-the-art methods used to train DNNs, that demonstrate the efficiency and effectiveness of our proposed new second-order methods.
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Multivariate analysis and GIS in generating vulnerability map of acid sulfate soils.Nguyen, Nga January 2015 (has links)
The study employed multi-variate methods to generate vulnerability maps for acid sulfate soils (AS) in the Norrbotten county of Sweden. In this study, the relationships between the reclassified datasets and each biogeochemical element was carefully evaluated with ANOVA Kruskal Wallis and PLS analysis. The sta-tistical results of ANOVA Kruskall-Wallis provided us a useful knowledge of the relationships of the preliminary vulnerability ranks in the classified datasets ver-sus the amount of each biogeochemical element. Then, the statistical knowledge and expert knowledge were used to generate the final vulnerability ranks of AS soils in the classified datasets which were the input independent variables in PLS analyses. The results of Kruskal-Wallis one way ANOVA and PLS analyses showed a strong correlation of the higher levels total Cu2+, Ni2+ and S to the higher vulnerability ranks in the classified datasets. Hence, total Cu2+, Ni2+ and S were chosen as the dependent variables for further PLS analyses. In particular, the Variable Importance in the Projection (VIP) value of each classified dataset was standardized to generate its weight. Vulnerability map of AS soil was a result of a lineal combination of the standardized values in the classified dataset and its weight. Seven weight sets were formed from either uni-variate or multi-variate PLS analyses. Accuracy tests were done by testing the classification of measured pH values of 74 soil profiles with different vulnerability maps and evaluating the areas that were not the AS soil within the groups of medium to high AS soil probability in the land-cover and soil-type datasets. In comparison to the other weight sets, the weight set of multi-variate PLS analysis of the matrix of total Ni2+& S or total Cu2+& S had the robust predictive performance. Sensitivity anal-ysis was done in the weight set of total Ni2+& S, and the results of sensitivity analyses showed that the availability of ditches, and the change in the terrain sur-faces, the altitude level, and the slope had a high influence to the vulnerability map of AS soils. The study showed that using multivariate analysis was a very good approach methodology for predicting the probability of acid sulfate soil.
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Minimax D-optimal designs for regression models with heteroscedastic errorsYzenbrandt, Kai 20 April 2021 (has links)
Minimax D-optimal designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible misspecification of the error variance in the model. We propose a flexible assumption for the error variance and use a minimax approach to define robust designs. As usual it is hard to find robust designs analytically, since the associated design problem is not a convex optimization problem. However, the minimax D-optimal design problem has an objective function as a difference of two convex functions. An effective algorithm is developed to compute minimax D-optimal designs under the least squares estimator and generalized least squares estimator. The algorithm can be applied to construct minimax D-optimal designs for any linear or nonlinear regression model with heteroscedastic errors. In addition, several theoretical results are obtained for the minimax D-optimal designs. / Graduate
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The Emerging Organizational Role of the Maintenance Function: A Strategic PerspectiveGomes, Carlos F., Yasin, Mahmoud M., Simões, Jorge M. 16 February 2021 (has links)
Purpose: With the growing importance of performance measurement and management, this exploratory study intends to examine the practices of maintenance managers with regards to maintenance measures, as used in their organizations. In this process, the study attempts to uncover the relevant maintenance performance dimensions from the perspectives of the surveyed managers. In addition, the mediating effect of information availability on the main performance measures utilization is studied. Design/methodology/approach: The research at hand is survey-based. It utilizes the responses of a sample of ninety-five (95) experienced maintenance managers to identify the most relevant maintenance performance measures. Factor analysis is then utilized to uncover the important dimensions of performance, as seen by the respondents. Additionally, using the Partial Least Squares method, several models were studied. Findings: The findings of this exploratory research appear to suggest that maintenance managers are beginning to broaden their perspective with regard to performance management. While machine and plant-related performance measures are still emphasized, maintenance managers are slowly moving toward a wider organizational orientation. While the manufacturing organizations are becoming more and more customer-oriented open systems, the maintenance function of these organizations is still, for the most part, operating under the semi-open system orientation. Overall, it appears that an emerging maintenance strategy is slowly taking shape. Research limitations/implications: For the most part, performance measures and measurement related to maintenance have not received enough attention from researchers. Therefore, the literature dealing with the different facets of performance in maintenance has not been forthcoming. The study attempts to fill this apparent gap in the literature. This is important, as maintenance managers are being asked to contribute to the achievement of the competitive strategies of their organizations. Therefore, they must quickly learn how to view maintenance from a coherent strategic organizational perspective. Such a perspective should help in integrating the maintenance, resources, capabilities, and technical know-how in order to serve the strategic goal of their organization. The research at hand is limited to a sample from Portugal. Therefore, the results and conclusions must be interpreted accordingly. Practical implications: As maintenance managers struggle to move from a machine-orientation to a more organizational-wide strategic orientation, they are often left with many questions and few answers. This study attempts to bring this problem to the spotlight so that it can receive more systematic empirical and practical research. In this context, the role of maintenance managers in the process of organizational strategy formulation should be examined. Originality/value: The study presented in this article has practical, as well as theoretical contributions. It deals with an area of performance measurement, which so far has been relatively ignored. It uses a system orientation (closed vs open), in addition to the strategic orientation (single vs multi-faceted strategy) in order to shed some light on the need to have consistency between the nature of the system and its strategic objective.
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Information Content in Data Sets: A Review of Methods for Interrogation and Model ComparisonBanks, H. Thomas, Joyner, Michele L. 01 January 2018 (has links)
In this reviewwe discuss methodology to ascertain the amount of information in given data sets with respect to determination of model parameters with desired levels of uncertainty.We do this in the context of least squares (ordinary,weighted, iterative reweightedweighted or "generalized", etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in the biological and environmental sciences.
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AIC Under the Framework of Least Squares EstimationBanks, H. T., Joyner, Michele L. 01 December 2017 (has links)
In this note we explain the use of the Akiake Information Criterion and its related model comparison indices (usually derived for maximum likelihood estimator inverse problem formulations) in the context of least squares (ordinary, weighted, iterative weighted or “generalized”, etc.) based inverse problem formulations. The ideas are illustrated with several examples of interest in biology.
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Impact of Microcredit Program on Women's Empowerment in Rural BangladeshChoudhury, Gias Uddin Ahmed January 2020 (has links)
Background – This study is an attempt to explore the relationship between microcredit and the socio-economic empowerment of women in rural Bangladesh. Microcredit is simply the extension of a small amount of collateral-free institutional loans to jointly liable poor group members to generate employment and income enhancing activities. As it is too difficult for poor members to get loan from the formal credit institutions, Grameen Bank (GB) or other Non-Government Organizations (NGOs) provide small loans to vulnerable groups of the society by which they are expected to empower over his counterparts. Research questions – RQ1: How does micro-credit affect different indicators of women empowerment in the rural areas of Bangladesh? RQ2– Is the impact different from the male counterparts in the sample households? Purpose – This study is an effort to find the impact of microcredit on a number of indicators of women’s empowerment in the rural areas in Bangladesh. Methodology – Quantitative Regression Techniques such as Ordinary Least Square (OLS) and Instrumental Variable (IV) method have been applied to get the relationship between microcredit and women empowerment. Conclusion – Applying nationally representative cross-section survey data, Bangladesh Integrated Household Survey (BIHS) 2015, this thesis is intended to find the causal linkage between microcredit and women empowerment’s with different dimensions of women’s decisions are taken as empowerment indicators: production, resources, income, leadership, savings and time. The analysis has been conducted at the household level. The study assumes that women empowerment is endogenous. After controlling for endogeneity in the estimation by using an instrumental variable (IV) ‘distance to the market’ this study finds a significant relationship between microcredit and different dimensions of women’s empowerment. Participation in the microcredit program is found to be significant in explaining some of the outcome indicators of empowerment for the sampled households.
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Sarvate-beam group divisible designs and related multigraph decomposition problemsNiezen, Joanna 30 September 2020 (has links)
A design is a set of points, V, together with a set of subsets of V called blocks. A classic type of design is a balanced incomplete block design, where every pair of points occurs together in a block the same number of times. This ‘balanced’ condition can be replaced with other properties. An adesign is a design where instead every pair of points occurs a different number of times together in a block. The number of times a specified pair of points occurs together is called the pair frequency.
Here, a special type of adesign is explored, called a Sarvate-Beam design, named after its founders D.G. Sarvate and W. Beam. In such an adesign, the pair frequencies cover an interval of consecutive integers. Specifically the existence of Sarvate-Beam group divisible designs are investigated. A group divisible design, in the usual sense, is a set of points and blocks where the points are partitioned into subsets called groups. Any pair of points contained in a group have pair frequency zero and pairs of points from different groups have pair frequency one. A Sarvate-Beam group divisible design, or SBGDD, is a group divisible design where instead the frequencies of pairs from different groups form a set of distinct nonnegative consecutive integers. The SBGDD is said to be uniform when the groups are of equal size.
The main result of this dissertation is to completely settle the existence question for uniform SBGDDs with blocks of size three where the smallest pair frequency, called the starting frequency, is zero. Higher starting frequencies are also considered and settled for all positive integers except when the SBGDD is partitioned into eight groups where a few possible exceptions remain.
A relationship between these designs and graph decompositions is developed and leads to some generalizations. The use of matrices and linear programming is also explored and give rise to related results. / Graduate
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