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  • Online Resource  (3)
  • Wissenschaftspark Albert Einstein  (3)
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  • 1
    Online Resource
    Online Resource
    Cambridge :Cambridge University Press,
    UID:
    almahu_9948233434802882
    Format: 1 online resource (xx, 482 pages) : , digital, PDF file(s).
    ISBN: 9781316145098 (ebook)
    Content: Hydrocarbon production, gas recovery from shale, CO2 storage and water management have a common scientific underpinning: multiphase flow in porous media. This book provides a fundamental description of multiphase flow through porous rock, with emphasis on the understanding of displacement processes at the pore, or micron, scale. Fundamental equations and principal concepts using energy, momentum, and mass balance are developed, and the latest developments in high-resolution three-dimensional imaging and associated modelling are explored. The treatment is pedagogical, developing sound physical principles to predict flow and recovery through complex rock structures, while providing a review of the recent literature. This systematic approach makes it an excellent reference for those who are new to the field. Inspired by recent research, and based on courses taught to thousands of students and professionals from around the world, it provides the scientific background necessary for a quantitative assessment of multiphase subsurface flow processes, and is ideal for hydrology and environmental engineering students, as well as professionals in the hydrocarbon, water and carbon storage industries.
    Note: Title from publisher's bibliographic system (viewed on 28 Feb 2017).
    Additional Edition: Print version: ISBN 9781107093461
    Language: English
    Subjects: Physics , Earth Sciences
    RVK:
    RVK:
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (URL des Erstveröffentlichers)
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  • 2
    UID:
    kobvindex_GFZ166673425X
    Format: 1 Online-Ressource (xxiii, 346 Seiten) , Illustrationen, Diagramme, Karten (überwiegend farbig)
    Edition: Tthird edition
    ISBN: 9783030104665 , 978-3-030-10466-5
    Content: It is not so long ago (a mere 17,000 years – a blink in geologic time) that vast areas of the Northern Hemisphere were covered with ice sheets up to two miles thick, lowering the oceans by more than 120 m. By 11,000 years ago, most of the ice was gone. Evidence from polar ice cores and ocean sediments show that Ice Ages were persistent and recurrent over the past 800,000 years. The data suggests that Ice Ages were the normal state, and were temporarily interrupted by interglacial warm periods about nine times during this period. Quasi-periodic variations in the Earth cause the solar input to high northern latitudes to vary with time over thousands of years. The widely accepted Milankovitch theory implies that the interglacial warm periods are associated with high solar input to high northern latitudes. However, many periods of high solar input to high northern latitudes occur during Ice Ages while the ice sheets remain. The data also indicates that Ice Ages will persist regardless of solar input to high northern latitudes, until several conditions are met that are necessary to generate a termination of an Ice Age. An Ice Age will not terminate until it has been maturing for many tens of thousands of years leading to a reduction of the atmospheric CO2 concentration to less than 200 ppm. At that point, CO2 starvation coupled with lower temperatures will cause desertification of marginal regions, leading to the generation of large quantities of dust. High winds transfer this dust to the ice sheets greatly increasing their solar absorptivity, and at the next up-lobe in the solar input to high northern latitudes, solar power melts the ice sheets over about a 6,000-year interval. A warm interglacial period follows, during which dust levels drop remarkably. Slowly but surely, ice begins accumulating again at high northern latitudes and an incipient new Ice Age begins. This third edition presents data and models to support this theory
    Note: Contents 1 History and Description of Ice Ages 1.1 Discovery of Ice Ages 1.2 Description of Ice Sheets 1.3 Vegetation During LGM 1.3.1 LGM Climate 1.3.2 Global Flora 1.3.3 Ice Age Forests 1.4 Vegetation and Dust Generation During the LGM 1.4.1 Introduction: Effect of Low CO2 on Plants 1.4.2 C3 and C4 Flora Differences 1.4.3 Effects of Low CO2 on Tree Lines 1.4.4 Source of the LGM Dust 2 Variability of the Earth’s Climate 2.1 Factors that Influence Global Climate 2.2 Stable Extremes of the Earth’s Climate 2.3 Ice Ages in the Recent Geological Past 3 Ice Core Methodology 3.1 History of Ice Core Research 3.2 Dating Ice Core Data 3.2.1 Introduction 3.2.2 Age Markers 3.2.3 Counting Layers Visually 3.2.4 Layers Determined by Measurement 3.2.5 Ice Flow Modeling 3.2.6 Other Dating Methods 3.2.7 Synchronization of Dating of Ice Cores from Greenland and Antarctica 3.2.8 GISP2 Experience 3.2.9 Tuning 3.2.10 Flimsy Logic 3.3 Processing Ice Core Data 3.3.1 Temperature Estimates from Ice Cores 3.3.2 Temperature Estimates from Borehole Models 3.3.3 Climate Variations 3.3.4 Trapped Gases 4 Ice Core Data 4.1 Greenland Ice Core Historical Temperatures 4.2 Antarctica Ice Core Historical Temperatures 4.2.1 Vostok and EPICA Data 4.2.2 Homogeneity of Antarctic Ice Cores 4.3 North-South Synchrony 4.3.1 Direct Comparison of Greenland and Antarctica Ice Core Records 4.3.2 Sudden Changes 4.3.3 Interpretation of Sudden Change in Terms of Ocean Circulation 4.3.4 Seasonal Variability of Precipitation 4.4 Data from High-Elevation Ice Cores 4.5 Carbon Dioxide 4.5.1 Measurements 4.5.2 Explanations 4.6 Dust in Ice Cores 5 Ocean Sediment Data 5.1 Introduction 5.2 Chronology 5.3 Universality of Ocean Sediment Data 5.4 Summary of Ocean Sediment Ice Volume Data 5.5 Comparison of Ocean Sediment Data with Polar Ice Core Data 5.6 Historical Sea Surface Temperatures 5.7 Ice-Rafted Debris 6 Other Data Sources 6.1 Devil’s Hole 6.1.1 Devil’s Hole Data 6.1.2 Comparison of Devil’s Hole Data with Ocean Sediment Data 6.1.3 Devil’s Hole: Global or Regional Data? 6.1.4 Comparison of Devil’s Hole Data with Vostok Data 6.1.5 The Continuing Controversy 6.2 Speleothems in Caves 6.3 Magnetism in Rocks and Loess 6.3.1 Magnetism in Loess 6.3.2 Rock Magnetism in Lake Sediments 6.4 Pollen Records 6.5 Physical Indicators 6.5.1 Ice Sheet Moraines 6.5.2 Coral Terraces 6.5.3 Mountain Glaciers 6.6 Red Sea Sediments 7 Overview of the Various Models for Ice Ages 7.1 Introduction 7.2 Variability of the Sun 7.3 Astronomical Theory 7.4 Volcanism 7.5 Greenhouse Gases 7.6 Role of the Oceans 7.6.1 Glacial-Interglacial Cycles: The Consensus View 7.6.2 Sudden Climate Change - The Consensus View 7.6.3 Wunsch’s Objections 7.7 Models Based on Clouds 7.7.1 Extraterrestrial Dust Accretion 7.7.2 Clouds Induced by Cosmic Rays 7.7.3 Ocean–Atmosphere Model 7.8 Models Based on the Southern Hemisphere 8 Variability of the Earth’s Orbit: Astronomical Theory 8.1 Introduction 8.2 Variability of the Earth’s Orbit 8.2.1 Variability Within the Orbital Plane 8.2.2 Variability of the Orbital Plane 8.3 Calculation of Solar Intensities 8.4 Importance of Each Orbital Parameter 8.5 Historical Solar Irradiance at Higher Latitudes 8.6 Connection Between Solar Variability and Glaciation/Deglaciation Cycles According to Astronomical Theory 8.6.1 Models for Ice Volume 8.6.2 Review of the Imbries’ Model 8.6.3 Memory Model 8.6.4 Modification of Paillard Model 8.7 Models Based on Eccentricity or Obliquity 8.7.1 A Model Based on Eccentricity 8.7.2 The Middle-Pleistocene Transition (MPT) 9 Comparison of Astronomical Theory with Data 9.1 Ice Volume Versus Solar Input 9.2 Spectral Analysis 9.2.1 Introduction 9.2.2 Spectral Analysis of Solar and Paleoclimate Data 10 Interglacials 11 Terminations of Ice Ages 11.1 Abstract 11.2 Background 11.3 Terminations 11.4 North or South (or Both)? 11.5 Models Based on CO 2 and the Southern Hemisphere 11.6 Climate Models for Terminations of Ice Ages 11.7 Model Based on Solar Amplitudes 11.8 Dust as the Driver for Terminations 11.8.1 Introduction 11.8.2 Antarctic Dust Data 11.8.3 Correlation of Ice Core Dust Data with Terminations 11.8.4 Dust Levels on the Ice Sheets 11.8.5 Optical Properties of Surface Deposited Dust 11.8.6 Source of the Dust 11.8.7 Ice Sheet Margins 11.9 Model Based on Solar Thresholds 11.10 The Milankovitch Model Versus the Most Likely Model 11.10.1 Criteria for a Theory 11.10.2 The “Milankovitch” Model 11.10.3 The Most Likely Model 11.10.4 Unanswered Questions 12 Status of Our Understanding References Index
    Language: English
    Subjects: Earth Sciences , Geography
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    Keywords: Electronic books ; Lehrbuch
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  • 3
    UID:
    kobvindex_GFZ1772255661
    Format: 1 Online-Ressource (xv, 229 Seiten) , Illustrationen, Diagramme
    ISBN: 9783030780555 , 978-3-030-78055-5
    ISSN: 2510-1307 , 2510-1315
    Series Statement: Springer Textbooks in Earth Sciences, Geography and Environment
    Content: This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
    Note: Contents Part I Python for Geologists: A Kickoff 1 Setting Up Your Python Environment, Easily 1.1 The Python Programming Language 1.2 Programming Paradigms 1.3 A Local Python Environment for Scientific Computing 1.4 Remote Python Environments 1.5 Python Packages for Scientific Applications 1.6 Python Packages Specifically Developed for Geologists 2 Python Essentials for a Geologist 2.1 Start Working with IPython Console 2.2 Naming and Style Conventions 2.3 Working with Python Scripts 2.4 Conditional Statements, Indentation, Loops, and Functions 2.5 Importing External Libraries 2.6 Basic Operations and Mathematical Functions 3 Solving Geology Problems Using Python: An Introduction 3.1 My First Binary Diagram Using Python 3.2 Making Our First Models in Earth Science 3.3 Quick Intro to Spatial Data Representation Part II Describing Geological Data 4 Graphical Visualization of a Geological Data Set 4.1 Statistical Description of a Data Set: Key Concepts 4.2 Visualizing Univariate Sample Distributions 4.3 Preparing Publication-Ready Binary Diagrams 4.4 Visualization of Multivariate Data: A First Attempt 5 Descriptive Statistics 1: Univariate Analysis 5.1 Basics of Descriptive Statistics 5.2 Location 5.3 Dispersion or Scale 5.4 Skewness 5.5 Descriptive Statistics in Pandas 5.6 Box Plots 6 Descriptive Statistics 2: Bivariate Analysis 6.1 Covariance and Correlation 6.2 Simple Linear Regression 6.3 Polynomial Regression 6.4 Nonlinear Regression Part III Integrals and Differential Equations in Geology 7 Numerical Integration 7.1 Definite Integrals 7.2 Basic Properties of Integrals 7.3 Analytical and Numerical Solutions of Definite Integrals 7.4 Fundamental Theorem of Calculus and Analytical Solutions 7.5 Numerical Solutions of Definite Integrals 7.6 Computing the Volume of Geological Structures 7.7 Computing the Lithostatic Pressure 8 Differential Equations 8.1 Introduction 8.2 Ordinary Differential Equations 8.3 Numerical Solutions of First-Order Ordinary Differential Equations 8.4 Fick’s Law of Diffusion—A Widely Used Partial Differential Equation Part IV Probability Density Functions and Error Analysis 9 Probability Density Functions and Their Use in Geology 9.1 Probability Distribution and Density Functions 9.2 The Normal Distribution 9.3 The Log-Normal Distribution 9.4 Other Useful PDFs for Geological Applications 9.5 Density Estimation 9.6 The Central Limit Theorem and Normal Distributed Means 10 Error Analysis 10.1 Dealing with Errors in Geological Measurements 10.2 Reporting Uncertainties in Binary Diagrams 10.3 Linearized Approach to Error Propagation 10.4 The Mote Carlo Approach to Error Propagation Part V Robust Statistics and Machine Learning 11 Introduction to Robust Statistics 11.1 Classical and Robust Approaches to Statistics 11.2 Normality Tests 11.3 Robust Estimators for Location and Scale 11.4 Robust Statistics in Geochemistry 12 Machine Learning 12.1 Introduction to Machine Learning in Geology 12.2 Machine Learning in Python 12.3 A Case Study of Machine Learning in Geology Appendix A: Python Packages and Resources for Geologists Appendix B: Introduction to Object Oriented Programming Appendix C: The Matplotlib Object Oriented API Appendix D: Working with Pandas Further Readings
    Language: English
    Subjects: Earth Sciences
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    Keywords: Lehrbuch ; Electronic books
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