Note: Cover -- Half-title -- Title page -- Copyright information -- Table of contents -- Preface -- Acknowledgments -- 1 Introduction -- 1.1 What is the value of information? -- 1.2 Motivating examples from the Earth sciences -- 1.3 Contributions of this book -- 1.4 Organization -- 1.5 Intended audience and prerequisites -- 1.6 Bibliographic notes -- 2 Statistical models and methods -- 2.1 Uncertainty quantification, information gathering, and data examples -- 2.2 Notation and probability models -- 2.2.1 Univariate probability distributions -- 2.2.2 Multivariate probability distributions -- 2.3 Conditional probability, graphical models, and Bayes' rule -- 2.3.1 Conditional probability -- 2.3.2 Graphical models -- 2.3.3 Bayesian updating from data -- 2.3.4 Examples -- Treasure Island: The pirate example -- Gotta get myself connected: Bayesian network example -- Never break the chain: Markov chain example -- For whom the bell tolls: Gaussian projects example -- 2.4 Inference of model parameters -- 2.4.1 Maximum likelihood estimation -- 2.4.2 Examples -- I love rock and ore: mining oxide grade example -- Never break the chain: Markov chain example -- 2.5 Monte Carlo methods and other approximation techniques -- 2.5.1 Analysis by simulation -- 2.5.2 Solving integrals -- 2.5.3 Sampling methods -- 2.5.4 Example -- Risky business: petroleum prospect risking example -- 2.6 Bibliographic notes -- Models -- Estimation and sampling -- 3 Decision analysis -- 3.1 Background -- 3.2 Decision situations: terminology and notation -- 3.2.1 Decisions, uncertainties, and values -- 3.2.2 Utilities and certain equivalent -- 3.2.3 Maximizing expected utility -- 3.2.4 Examples -- Treasure island: the pirate example -- For whom the bell tolls: Gaussian projects example -- 3.3 Graphical models -- 3.3.1 Decision trees -- 3.3.2 Influence diagrams -- 3.3.3 Examples. , For whom the bell tolls: Gaussian projects example -- MacKenna's gold: oil and gold example -- Time after time: time-lapse seismic example -- Value from 4-D seismic monitoring -- Influence diagrams for 4-D seismic monitoring -- Observable property nodes -- Reservoir property nodes -- Seismic property nodes -- 3.4 Value of information -- 3.4.1 Definition -- 3.4.2 Perfect versus imperfect information -- 3.4.3 Relevant, material, and economic information -- 3.4.4 Examples -- Treasure island: the pirate example -- For whom the bell tolls: Gaussian projects example -- 3.5 Bibliographic notes -- Decision analysis fundamentals -- Graphical models -- VOI fundamentals -- VOI for canonical problems -- Computational issues and application reviews -- 4 Spatial modeling -- 4.1 Goals of stochastic modeling of spatial processes -- 4.2 Random fields, variograms, and covariance -- 4.3 Prediction and simulation -- 4.3.1 Spatial prediction and Kriging -- 4.3.2 Common geostatistical stochastic simulation methods -- 4.4 Gaussian models -- 4.4.1 The spatial regression model -- 4.4.2 Optimal spatial prediction: Kriging -- 4.4.3 Multivariate hierarchical spatial regression model -- 4.4.4 Examples -- Norwegian wood: forestry example -- I love rock and ore: mining oxide grade example -- 4.5 Non-Gaussian response models and hierarchical spatial models -- 4.5.1 Skew-normal models -- 4.5.2 Spatial generalized linear models -- 4.5.3 Example -- We will rock you: rock hazard example -- 4.6 Categorical spatial models -- 4.6.1 Indicator random variables -- 4.6.2 Truncated Gaussian and pluri-Gaussian models -- 4.6.3 Categorical Markov random field models -- 4.6.4 Example -- Black gold in a white plight: reservoir characterization example -- 4.7 Multiple-point geostatistics -- 4.7.1 Algorithms -- 4.7.2 Example -- Go with the flow: petroleum simulation example -- 4.8 Bibliographic notes. , Traditional geostatistics books describing variogram methods, Kriging, and simulation -- Sequential simulation techniques and related topics -- Spatial statistics -- Non-Gaussian response models -- Markov random fields -- Multiple-point geostatistics -- Spatiotemporal models -- 5 Value of information in spatial decision situations -- 5.1 Introduction -- 5.1.1 Spatial decision situations -- 5.1.2 Information gathering in spatial decision situations -- 5.1.3 Overview of models -- 5.2 Value of information: a formulation for static models -- 5.2.1 Prior value -- 5.2.2 Posterior value -- Perfect information -- Imperfect information -- 5.2.3 Special cases: an overview -- 5.3 Special case: low decision flexibility and decoupled value -- 5.3.1 Prior value -- 5.3.2 Posterior value -- 5.3.3 Computational notes -- 5.3.4 Example -- Norwegian wood: forestry example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 5.4 Special case: high decision flexibility and decoupled value -- 5.4.1 Prior value -- 5.4.2 Posterior value -- 5.4.3 Computational notes -- 5.4.4 Examples -- Never break the chain: Markov chain example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- The tree amigos: conservation biology example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- Norwegian wood: forestry example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 5.5 Special case: low decision flexibility and coupled value -- 5.5.1 Prior value -- 5.5.2 Posterior value -- 5.5.3 Computational notes -- 5.5.4 Example -- Go with the flow: petroleum simulation example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 5.6 Special case: high decision flexibility and coupled value. , 5.6.1 Prior value -- 5.6.2 Posterior value -- 5.6.3 Computational notes -- 5.6.4 Example -- Frozen: hydropower example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 5.7 More complex decision situations -- 5.7.1 Generalized risk preferences -- 5.7.2 Additional constraints -- The tree amigos: conservation biology example -- 5.7.3 Sequential decision situations -- Gotta get myself connected: Bayesian network example -- 5.8 Sequential information gathering -- For whom the bell tolls: Gaussian projects example -- 5.9 Other information measures -- 5.9.1 Entropy -- 5.9.2 Prediction variance -- Norwegian wood: forestry example -- 5.9.3 Prediction error -- The tree amigos: conservation biology example -- 5.10 Bibliographic notes -- Low decision flexibility and decoupled value -- Low decision flexibility and coupled value -- High decision flexibility and decoupled value -- High decision flexibility and coupled value -- More complex decision situations -- Other information measures -- 6 Earth sciences applications -- 6.1 Workflow -- 6.2 Exploration of petroleum prospects -- 6.2.1 Gotta get myself connected: Bayesian network example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.2.2 Basin street blues: basin modeling example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.2.3 Risky business: petroleum prospect risking example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.3 Reservoir characterization from geophysical data -- 6.3.1 Black gold in a white plight: reservoir characterization example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.3.2 Reservoir dogs: seismic and electromagnetic data example -- Framing the decision situation. , Information gathering -- Modeling -- VOI analysis -- 6.4 Mine planning and safety -- 6.4.1 I love rock and ore: mining oxide grade example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.4.2 We will rock you: rock hazard example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.5 Groundwater management -- 6.5.1 Salt water wells in my eyes: groundwater management example -- Framing the decision situation -- Information gathering -- Modeling -- VOI analysis -- 6.6 Bibliographic notes -- Petroleum -- Mining planning and safety -- Groundwater, hydrology, and geothermal resources -- Environmental applications -- Biological conservation, forestry, and fishing -- Agriculture and climate forecasting -- 7 Problems and projects -- 7.1 Problems and tutorial hands-on projects -- 7.1.1 Problem sets -- 7.1.2 Hands-on projects -- The tree amigos: conservation biology example -- Part I: parameter specification -- Part II: value of information -- Go with the flow: petroleum simulation example -- Part I: downloading realization outputs -- Part II: generate realizations from geologic scenario -- Part III: VOI analysis using approximate Bayesian computing -- Frozen: hydropower example -- Time after time: time-lapse seismic example -- Norwegian wood: forestry example -- Part I: parameter estimation and Kriging -- Part II: VOI analysis and spatial design -- 7.2 Hands on: exploration of petroleum prospects -- 7.2.1 Gotta get myself connected: Bayesian network example -- Part I: small network -- Part II: North Sea network with 25 segments -- 7.2.2 Basin street blues: basin modeling example -- 7.2.3 Risky business: petroleum prospect risking example -- 7.3 Hands on: reservoir characterization from geophysical data -- 7.3.1 Black gold in a white plight: reservoir characterization example. , Part I: modeling and prediction from seismic amplitude versus offset data.