253 research outputs found

    Wavelet q-Fisher Information for Scaling Signal Analysis

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    This article first introduces the concept of wavelet q-Fisher information and then derives a closed-form expression of this quantifier for scaling signals of parameter α. It is shown that this information measure appropriately describes the complexities of scaling signals and provides further analysis flexibility with the parameter q. In the limit of q→1, wavelet q-Fisher information reduces to the standard wavelet Fisher information and for q > 2 it reverses its behavior. Experimental results on synthesized fGn signals validates the level-shift detection capabilities of wavelet q-Fisher information. A comparative study also shows that wavelet q-Fisher information locates structural changes in correlated and anti-correlated fGn signals in a way comparable with standard breakpoint location techniques but at a fraction of the time. Finally, the application of this quantifier to H.263 encoded video signals is presented.Consejo Nacional de Ciencia y TecnologíaFOMIX-COQCY

    Geophysics and Ocean Waves Studies

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    The book “Geophysics and Ocean Waves Studies” presents the collected chapters in two sections named “Geophysics” and “Ocean Waves Studies”. The first section, “Geophysics”, provides a thorough overview of using different geophysical methods including gravity, self-potential, and EM in exploration. Moreover, it shows the significance of rock physics properties and enhanced oil recovery phases during oil reservoir production. The second section, “Ocean Waves Studies”, is intended to provide the reader with a strong description of the latest developments in the physical and numerical description of wind-generated and long waves, including some new features discovered in the last few years. The section is organized with the aim to introduce the reader from offshore to nearshore phenomena including a description of wave dissipation and large-scale phenomena (i.e., storm surges and landslide-induced tsunamis). This book shall be of great interest to students, scientists, geologists, geophysicists, and the investment community

    Accelerating inference in cosmology and seismology with generative models

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    Statistical analyses in many physical sciences require running simulations of the system that is being examined. Such simulations provide complementary information to the theoretical analytic models, and represent an invaluable tool to investigate the dynamics of complex systems. However, running simulations is often computationally expensive, and the high number of required mocks to obtain sufficient statistical precision often makes the problem intractable. In recent years, machine learning has emerged as a possible solution to speed up the generation of scientific simulations. Machine learning generative models usually rely on iteratively feeding some true simulations to the algorithm, until it learns the important common features and is capable of producing accurate simulations in a fraction of the time. In this thesis, advanced machine learning algorithms are explored and applied to the challenge of accelerating physical simulations. Various techniques are applied to problems in cosmology and seismology, showing benefits and limitations of such an approach through a critical analysis. The algorithms are applied to compelling problems in the fields, including surrogate models for the seismic wave equation, the emulation of cosmological summary statistics, and the fast generation of large simulations of the Universe. These problems are formulated within a relevant statistical framework, and tied to real data analysis pipelines. In the conclusions, a critical overview of the results is provided, together with an outlook over possible future expansions of the work presented in the thesis

    Causal inference and forescasting methods for climate data nalysis

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    To advance time series forecasting we need to progress on multiple fronts. In this thesis, we develop algorithms to identify causal relations which allow to identify the driving processes containing useful information for the prediction of the process of interest. Complementing this, machine learning algorithms allow to exploit such information to build data-driven forecast models, and to correct the prediction of dynamical models. The identification from time series analysis of reliable indicators of causal relationships, is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years, many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters, limit their applicability. In this thesis, we propose a computationally efficient measure for causality testing, with the goal of overcoming the limitations of information-theoretic measures, due their high computational cost. The proposed metric is useful when causality networks need to be inferred from the analysis of a large number of relatively short time series. It can also be very useful for the selection of the inputs for the machine learning algorithms; in fact, it allows to identify those processes which contain useful information for the prediction of a given process. This is particularly useful for systems composed of a large number of processes, whose interactions are poorly understood. On the other hand, the socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden-Julian Oscillation (MJO), which is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, is particularly important because it can promote or enhance extreme events in both, the tropics and the extratropics. Currently, the prediction skill of MJO is receiving a lot of attention, and in this thesis we take two machine learning approaches; first we use machine learning as a stand-alone technique to analyze observed data, showing that two artificial neural networks, a feed-forward neural network and a recurrent neural network, allow a competitive prediction, yet not exceeding the skill of the state-of-art dynamical models. Then, we combine dynamical models with machine learning, which allows to improve the predictions of the best dynamical model. In particular, machine learning allows to improve the prediction of the MJO intensity and geographical localizationPara avanzar en el pronóstico de series temporales, es necesario avanzar en múltiples frentes. En esta tesis, desarrollamos algoritmos para descubrir relaciones causales que identifican los procesos que actúan como fuentes de información y pueden ayudar a mejorar la predicción del proceso de interés. Por otro lado, los algoritmos de aprendizaje automático permiten explotar dicha información para mejorar la predicción de los modelos dinámicos. La identificación de relaciones de causalidad a partir de series temporales es esencial en muchas disciplinas. Los desafíos en este ámbito son distinguir la correlación de la causalidad, así como diferenciar entre las interacciones directas e indirectas. A lo largo de los años se han propuesto numerosos métodos de inferencia causal basados en la observación de datos. No obstante, su éxito depende de las características del sistema a investigar. A menudo, el coste computacional o el número de parámetros limitan su aplicabilidad. En esta tesis se propone una medida computacionalmente eficiente para el testeo de causalidad. La métrica que se propone resulta util cuando es necesario inferir causalidad a partir de análisis de un gran número de series temporales relativamente cortas. También puede resultar muy útil en la selección de entradas en los algoritmos de aprendizaje automático. De hecho, permite identificar aquellos procesos que contienen información útil en la predicción de cierto proceso dado. Por otro lado, el impacto socioeconómico de fenómenos meteorológicos extremos requiere el desarrollo de nuevas metodologías con el objetivo de obtener predicciones meteorológicas más precisas. La Oscilación de Madden-Julian (MJO) es el modo dominante de variabilidad en la atmósfera tropical en escalas temporales subestacionales, y puede promover o aumentar eventos extremos tanto en el trópico como el extratrópico. Actualmente, la prediccion de la MJO genera mucho interés. Por esta razon, en esta tesis se han escogido dos metodologías diferentes de aprendizaje automático. Primero, se han utilizado dos redes neuronales artificiales para analizar datos observacionales, una red neuronal feed-forward y una red neuronal recurrente. Se muestra que esto permite una predicción competitiva, pero sin superar la capacidad de los modelos dinámicos actuales. Por este motivo, en un segundo estudio se han combinado modelos dinámicos con aprendizaje automático, que permiten mejorar las predicciones del mejor modelo dinámico. En particular, el aprendizaje automático permite mejorar la predicción de la intensidad de MJO y su localización geográficaPostprint (published version

    Causal inference and forescasting methods for climate data nalysis

    Get PDF
    To advance time series forecasting we need to progress on multiple fronts. In this thesis, we develop algorithms to identify causal relations which allow to identify the driving processes containing useful information for the prediction of the process of interest. Complementing this, machine learning algorithms allow to exploit such information to build data-driven forecast models, and to correct the prediction of dynamical models. The identification from time series analysis of reliable indicators of causal relationships, is essential for many disciplines. Main challenges are distinguishing correlation from causality and discriminating between direct and indirect interactions. Over the years, many methods for data-driven causal inference have been proposed; however, their success largely depends on the characteristics of the system under investigation. Often, their data requirements, computational cost or number of parameters, limit their applicability. In this thesis, we propose a computationally efficient measure for causality testing, with the goal of overcoming the limitations of information-theoretic measures, due their high computational cost. The proposed metric is useful when causality networks need to be inferred from the analysis of a large number of relatively short time series. It can also be very useful for the selection of the inputs for the machine learning algorithms; in fact, it allows to identify those processes which contain useful information for the prediction of a given process. This is particularly useful for systems composed of a large number of processes, whose interactions are poorly understood. On the other hand, the socioeconomic impact of weather extremes draws the attention of researchers to the development of novel methodologies to make more accurate weather predictions. The Madden-Julian Oscillation (MJO), which is the dominant mode of variability in the tropical atmosphere on sub-seasonal time scales, is particularly important because it can promote or enhance extreme events in both, the tropics and the extratropics. Currently, the prediction skill of MJO is receiving a lot of attention, and in this thesis we take two machine learning approaches; first we use machine learning as a stand-alone technique to analyze observed data, showing that two artificial neural networks, a feed-forward neural network and a recurrent neural network, allow a competitive prediction, yet not exceeding the skill of the state-of-art dynamical models. Then, we combine dynamical models with machine learning, which allows to improve the predictions of the best dynamical model. In particular, machine learning allows to improve the prediction of the MJO intensity and geographical localizationPara avanzar en el pronóstico de series temporales, es necesario avanzar en múltiples frentes. En esta tesis, desarrollamos algoritmos para descubrir relaciones causales que identifican los procesos que actúan como fuentes de información y pueden ayudar a mejorar la predicción del proceso de interés. Por otro lado, los algoritmos de aprendizaje automático permiten explotar dicha información para mejorar la predicción de los modelos dinámicos. La identificación de relaciones de causalidad a partir de series temporales es esencial en muchas disciplinas. Los desafíos en este ámbito son distinguir la correlación de la causalidad, así como diferenciar entre las interacciones directas e indirectas. A lo largo de los años se han propuesto numerosos métodos de inferencia causal basados en la observación de datos. No obstante, su éxito depende de las características del sistema a investigar. A menudo, el coste computacional o el número de parámetros limitan su aplicabilidad. En esta tesis se propone una medida computacionalmente eficiente para el testeo de causalidad. La métrica que se propone resulta util cuando es necesario inferir causalidad a partir de análisis de un gran número de series temporales relativamente cortas. También puede resultar muy útil en la selección de entradas en los algoritmos de aprendizaje automático. De hecho, permite identificar aquellos procesos que contienen información útil en la predicción de cierto proceso dado. Por otro lado, el impacto socioeconómico de fenómenos meteorológicos extremos requiere el desarrollo de nuevas metodologías con el objetivo de obtener predicciones meteorológicas más precisas. La Oscilación de Madden-Julian (MJO) es el modo dominante de variabilidad en la atmósfera tropical en escalas temporales subestacionales, y puede promover o aumentar eventos extremos tanto en el trópico como el extratrópico. Actualmente, la prediccion de la MJO genera mucho interés. Por esta razon, en esta tesis se han escogido dos metodologías diferentes de aprendizaje automático. Primero, se han utilizado dos redes neuronales artificiales para analizar datos observacionales, una red neuronal feed-forward y una red neuronal recurrente. Se muestra que esto permite una predicción competitiva, pero sin superar la capacidad de los modelos dinámicos actuales. Por este motivo, en un segundo estudio se han combinado modelos dinámicos con aprendizaje automático, que permiten mejorar las predicciones del mejor modelo dinámico. En particular, el aprendizaje automático permite mejorar la predicción de la intensidad de MJO y su localización geográficaFísica computacional i aplicad

    Global Risks 2013, Eighth Edition

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    The World Economic Forum's Global Risks 2013 report is developed from an annual survey of over 1,000 experts from industry,government, academia and civil society who were asked to review a landscape of 50 global risks. The global risk that respondents rated most likely to manifest over the next 10 years is severe income disparity, while the risk rated as having the highest impact if it were to manifest is major systemic financial failure. There are also two risks appearing in the top five of both impact and likelihood - chronic fiscal imbalances and water supply crisis

    Development of a Python Library for Processing Seismic Time Series

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    Earthquakes occur around the world every day. This natural phenomena can result in enormous destruction and loss of life. However, at the same time, it is the primary source for studying Earth, the active planet. The seismic waves generated by earthquakes propagate deep into the Earth, carrying considerable information about the Earth’s structure, from the shallow depths in the crust to the core. The information transferred by seismic waves needs advanced signal processing and inversion tools to be converted into useful information about the Earths inner structures, from local to global scales. The ever­evolving interest for investigating more accurately the terrestrial system led to the development of advanced signal processing algorithms to extract optimal information from the recorded seismic waveforms. These algorithms use advanced numerical modeling to extract optimal information from the different seismic phases generated by earthquakes. The development of algorithms from a mathematical­physical point of view is of great interest; on the other hand, developing a platform for their implementation is also significant. This research aims to build a bridge between the development of purely theoretical ideas in seismology and their functional implementation. In this dissertation SeisPolPy, a high quality Python­based library for processing seismic waveforms is developed. It consists of the latest polarization analysis and filter algorithms to extract different seismic phases in the recorded seismograms. The algorithms range from the most common algorithms in the literature to a newly developed method, sparsity­promoting time­frequency filtering. In addition, the focus of the work is on the generation of high­quality synthetic seismic data for testing and evaluating the algorithms. SeisPolPy library, aims to provide seismology community a tool for separation of seismic phases by using high­resolution polarization analysis and filtering techniques. The research work is carried out within the framework of the Seismicity and HAzards of the sub­saharian Atlantic Margin (SHAZAM) project that requires high quality algorithms able to process the limited seismic data available in the Gulf of Guinea, the study area of the SHAZAM project.Terramotos ocorrem todos os dias em todo o mundo. Esta fenomeno natural pode vir a resultar numa enorme destruição e perda de vidas. No entanto, ao mesmo tempo, é a principal fonte para o estudo da Terra, o planeta activo. As ondas sísmicas geradas pelos terramotos propagam­se profundamente na Terra, levando informação considerável sobre a estrutura da Terra, desde as zonas de menor profundidade da crosta até ao núcleo. A informação transferida por ondas sísmicas necessita de processamento avançado de sinais e ferramentas de inversão para ser convertida em informação util sobre a estrutura interna da Terra, desde escalas locais a globais. O interesse sempre crescente em investigar com maior precisão o sistema terrestre levou ao desenvolvimento de algoritmos avançados de processamento de sinais para extrair informação óptima das formas de ondas sísmicas registadas. Estes algoritmos fazem uso de modelos numéricos avançados para extrair informação óptima das diferentes fases sísmicas geradas pelos terramotos. O desenvolvimento de algoritmos de um ponto de vista matemático­físico é de grande interesse; por outro lado, o desenvolvimento de uma plataforma para a sua implementação é também significativo. Esta investigação visa construir uma ponte entre o desenvolvimento de ideias puramente teóricas em sismologia e a sua implementação funcional. Com o decorrer desta dissertação foi desenvolvido o SeisPolPy, uma biblioteca de alta qualidade baseada em Python para o processamento de formas de ondas sísmicas. Consiste na mais recente análise de polarização e algoritmos de filtragem para extrair diferentes fases sísmicas nos sismogramas registados. Os algoritmos variam desde os algoritmos mais comuns na literatura até um método recentemente desenvolvido, que promove a frequência de filtragem por tempo e frequência. Além disso, o foco do trabalho é a geração de dados sísmicos sintéticos de alta qualidade para testar e avaliar os algoritmos. A biblioteca SeisPolPy, visa fornecer à comunidade sismológica uma ferramenta para a separação das fases sísmicas, utilizando técnicas de análise de polarização e filtragem de alta resolução. O trabalho de investigação é realizado no âmbito do projecto SHAZAM que requer algoritmos de alta qualidade que possuam a capacidade de processar os dados sísmicos, limitados, disponíveis no Golfo da Guiné, a área de estudo do projecto

    Analysis of the rainfall variability in the subtropical North Atlantic region: Bermuda, Canary Island, Madeira and Azores

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    Tesis presentada en la Universidad de Las Palmas de Gran Canaria, Programa de doctorado Física Fundamental y Aplicada. Departamento de Física[EN] This study presents an analysis of the rainfall in the subtropical North Atlantic region, proceeding as a reference the archipelagos of Bermuda, Canary Islands, Madeira and Azores. The spatial and seasonal variability and the annual cycle of the rainfall, on the basis of daily rainfall data records in the past decades with particular emphasis on the normal period 1981-2010, have been the main focus of this work. Particular importance has been given to the annual pattern, due to its crucial role in freshwater resources management.[ES] Este estudio presenta un análisis de la precipitación en la región subtropical del Atlántico Norte, tomando como referencia los archipiélagos de Bermudas, Canarias, Madeira y Azores. La variabilidad espacial y estacional, así como el ciclo anual de la lluvia en las pasadas décadas con particular énfasis en el periodo normal 1981-2010 han supuesto el centro de atención del mismo. Particular énfasis se ha dado también a la tendencia anual debido al papel crucial que tiene en la gestión de los recursos hídricos
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