内容

目次

Chapter 1: The Earth Resources Challenge 1.1 When challenges bring opportunities 1.2 Production planning and development for an oil field in Libya 1.3 Decision making under uncertainty for groundwater management in Denmark 1.4 Monitoring shallow geothermal systems in Belgium 1.5 Designing strategies for uranium remediation in the USA 1.6 Developing shale plays in North America 1.7 Synthesis: Data–Model–Prediction–Decision 1.8 References Chapter 2: Decision making under uncertainty 2.1 Introduction 2.2 Introductory example: the thumbtack game 2.3 Challenges in the decision–making process 2.4 Decision analysis as a science 2.5 Graphical tools 2.6 Value of information 2.7 References Chapter 3: Data Science for Geoscience 3.1 Introductory example 3.2 Basic Algebra 3.3 Basics of univariate & multi–variate probability theory & statistics 3.4 Decomposition of data 3.5 Orthogonal component analysis 3.6 Functional data analysis 3.7 Regression and Classification 3.8 Kernel methods 3.9 Cluster analysis 3.10 Monte Carlo & quasi Monte Carlo 3.11 Sequential Monte Carlo 3.12 Markov chain Monte Carlo 3.13 The bootstrap 3.14 References Chapter 4: Sensitivity Analysis 4.1 Introduction 4.2 Notation and application example 4.3 Screening techniques 4.4 Global SA methods 4.5 Quantifying impact of stochasticity in models 4.6 Summary 4.7 References Chapter 5: Bayesianism 5.1 Introduction 5.2 A historical perspective 5.3 Science as knowledge derived from facts, data or experience 5.4 The role of experiments – data 5.5 Induction vs deduction 5.6 Falsificationism 5.7 Paradigms 5.8 Bayesianism 5.9 Bayesianism in geological sciences 5.10 References Chapter 6: Geological priors & inversion 6.1 Introduction 6.2 The general discrete inverse problem 6.3 Prior model parameterization 6.4 Deterministic inversion 6.5 Bayesian inversion with geological priors 6.6 Geological priors in geophysical inversion 6.7 Geological priors in ensemble filtering methods 6.8 References Chapter 7: Bayesian Evidential Learning 7.1 The prediction problem revisited 7.2 Components of statistical learning 7.3 Bayesian Evidential Learning in Practice 7.4 References Chapter 8: Quantifying uncertainty in subsurface systems 8.1 Introduction 8.2 Production planning and development for an oil field in Libya 8.3 Decision making under uncertainty for groundwater management in Denmark 8.4 Monitoring shallow geothermal systems in Belgium 8.5 Designing uranium contaminant remediation in the USA 8.6 Developing shale plays in North America 8.7 References Chapter 9: Software & Implementation 9.1 Introduction 9.2 Model Generation 9.3 Forward Simulation 9.4 Post–Processing 9.5 References Chapter 10: Outlook 10.1 Introduction 10.2 Seven questions