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MARC Lista (tabellare)
Basic statistical concepts and methods for earth scientists [[electronic resource] /] / by Ricardo A. Olea
Basic statistical concepts and methods for earth scientists [[electronic resource] /] / by Ricardo A. Olea
Autore Olea R. A (Ricardo A.)
Edizione [Rev. and reprinted 2008.]
Pubbl/distr/stampa Reston, Va. : , : U.S. Geological Survey, , 2008
Descrizione fisica 191 pages : digital, PDF file
Collana Open-file report
Soggetto topico Statistics
Earth sciences - Statistical methods
Earth scientists - Education (Continuing education)
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910696771103321
Olea R. A (Ricardo A.)  
Reston, Va. : , : U.S. Geological Survey, , 2008
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geostatistics [[electronic resource] ] : Modeling Spatial Uncertainty
Geostatistics [[electronic resource] ] : Modeling Spatial Uncertainty
Autore Chil?s Jean-Paul
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, : Wiley, 2012
Descrizione fisica 1 online resource (740 p.)
Disciplina 550
550.72
Altri autori (Persone) DelfinerPierre
Collana Wiley Series in Probability and Statistics
Soggetto topico Earth sciences - Statistical methods
Earth sciences -- Statistical methods
Environmental engineering
Geology -- Statistical methods
Mining engineering
Geology
Earth & Environmental Sciences
Geology - General
ISBN 1-280-58852-7
9786613618351
1-118-13617-9
1-118-13618-7
1-118-13615-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Geostatistics: Modeling Spatial Uncertainty; Contents; Preface to the Second Edition; Preface to the First Edition; Abbreviations; Introduction; Types of Problems Considered; Description or Interpretation?; 1. Preliminaries; 1.1: Random Functions; 1.2: On the Objectivity of Probabilistic Statements; 1.3: Transitive Theory; 2. Structural Analysis; 2.1: General Principles; 2.2: Variogram Cloud and Sample Variogram; 2.3: Mathematical Properties of the Variogram; 2.4: Regularization and Nugget Effect; 2.5: Variogram Models; 2.6: Fitting a Variogram Model
2.7: Variography in the Presence of a Drift2.8: Simple Applications of the Variogram; 2.9: Complements: Theory of Variogram Estimation and Fluctuation; 3. Kriging; 3.1: Introduction; 3.2: Notations and Assumptions; 3.3: Kriging with a Known Mean; 3.4: Kriging with an Unknown Mean; 3.5: Estimation of a Spatial Average; 3.6: Selection of a Kriging Neighborhood; 3.7: Measurement Errors and Outliers; 3.8: Case Study: The Channel Tunnel; 3.9: Kriging Under Inequality Constraints; 4. Intrinsic Model of Order k; 4.1: Introduction; 4.2: A Second Look at the Model of Universal Kriging
4.3: Allowable Linear Combinations of Order k4.4: Intrinsic Random Functions of Order k; 4.5: Generalized Covariance Functions; 4.6: Estimation in the IRF Model; 4.7: Generalized Variogram; 4.8: Automatic Structure Identification; 4.9: Stochastic Differential Equations; 5. Multivariate Methods; 5.1: Introduction; 5.2: Notations and Assumptions; 5.3: Simple Cokriging; 5.4: Universal Cokriging; 5.5: Derivative Information; 5.6: Multivariate Random Functions; 5.7: Shortcuts; 5.8: Space-Time Models; 6. Nonlinear Methods; 6.1: Introduction; 6.2: Global Point Distribution
6.3: Local Point Distribution: Simple Methods6.4: Local Estimation by Disjunctive Kriging; 6.5: Selectivity and Support Effect; 6.6: Multi-Gaussian Change-of-Support Model; 6.7: Affine Correction; 6.8: Discrete Gaussian Model; 6.9: Non-Gaussian Isofactorial Change-of-Support Models; 6.10: Applications and Discussion; 6.11: Change of Support by the Maximum (C. Lantuéjoul); 7. Conditional Simulations; 7.1: Introduction and Definitions; 7.2: Direct Conditional Simulation of a Continuous Variable; 7.3: Conditioning by Kriging; 7.4: Turning Bands
7.5: Nonconditional Simulation of a Continuous Variable7.6: Simulation of a Categorical Variable; 7.7: Object-Based Simulations: Boolean Models; 7.8: Beyond Standard Conditioning; 7.9: Additional Topics; 7.10: Case Studies; Appendix; References; Index
Record Nr. UNINA-9910141310403321
Chil?s Jean-Paul  
Hoboken, : Wiley, 2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Geostatistics : modeling spatial uncertainty / / Jean-Paul Chiles, Pierre Delfiner
Geostatistics : modeling spatial uncertainty / / Jean-Paul Chiles, Pierre Delfiner
Autore Chiles Jean-Paul
Edizione [2nd ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, c2012
Descrizione fisica 1 online resource (740 p.)
Disciplina 550
550.72
Altri autori (Persone) DelfinerPierre
Collana Wiley series in probability and statistics
Soggetto topico Earth sciences - Statistical methods
Spatial analysis (Statistics)
ISBN 9786613618351
9781280588525
1280588527
9781118136171
1118136179
9781118136188
1118136187
9781118136157
1118136152
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Geostatistics: Modeling Spatial Uncertainty; Contents; Preface to the Second Edition; Preface to the First Edition; Abbreviations; Introduction; Types of Problems Considered; Description or Interpretation?; 1. Preliminaries; 1.1: Random Functions; 1.2: On the Objectivity of Probabilistic Statements; 1.3: Transitive Theory; 2. Structural Analysis; 2.1: General Principles; 2.2: Variogram Cloud and Sample Variogram; 2.3: Mathematical Properties of the Variogram; 2.4: Regularization and Nugget Effect; 2.5: Variogram Models; 2.6: Fitting a Variogram Model
2.7: Variography in the Presence of a Drift2.8: Simple Applications of the Variogram; 2.9: Complements: Theory of Variogram Estimation and Fluctuation; 3. Kriging; 3.1: Introduction; 3.2: Notations and Assumptions; 3.3: Kriging with a Known Mean; 3.4: Kriging with an Unknown Mean; 3.5: Estimation of a Spatial Average; 3.6: Selection of a Kriging Neighborhood; 3.7: Measurement Errors and Outliers; 3.8: Case Study: The Channel Tunnel; 3.9: Kriging Under Inequality Constraints; 4. Intrinsic Model of Order k; 4.1: Introduction; 4.2: A Second Look at the Model of Universal Kriging
4.3: Allowable Linear Combinations of Order k4.4: Intrinsic Random Functions of Order k; 4.5: Generalized Covariance Functions; 4.6: Estimation in the IRF Model; 4.7: Generalized Variogram; 4.8: Automatic Structure Identification; 4.9: Stochastic Differential Equations; 5. Multivariate Methods; 5.1: Introduction; 5.2: Notations and Assumptions; 5.3: Simple Cokriging; 5.4: Universal Cokriging; 5.5: Derivative Information; 5.6: Multivariate Random Functions; 5.7: Shortcuts; 5.8: Space-Time Models; 6. Nonlinear Methods; 6.1: Introduction; 6.2: Global Point Distribution
6.3: Local Point Distribution: Simple Methods6.4: Local Estimation by Disjunctive Kriging; 6.5: Selectivity and Support Effect; 6.6: Multi-Gaussian Change-of-Support Model; 6.7: Affine Correction; 6.8: Discrete Gaussian Model; 6.9: Non-Gaussian Isofactorial Change-of-Support Models; 6.10: Applications and Discussion; 6.11: Change of Support by the Maximum (C. Lantuéjoul); 7. Conditional Simulations; 7.1: Introduction and Definitions; 7.2: Direct Conditional Simulation of a Continuous Variable; 7.3: Conditioning by Kriging; 7.4: Turning Bands
7.5: Nonconditional Simulation of a Continuous Variable7.6: Simulation of a Categorical Variable; 7.7: Object-Based Simulations: Boolean Models; 7.8: Beyond Standard Conditioning; 7.9: Additional Topics; 7.10: Case Studies; Appendix; References; Index
Record Nr. UNINA-9910808398903321
Chiles Jean-Paul  
Hoboken, N.J., : Wiley, c2012
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Introduzione alla geostatistica / Donato Posa
Introduzione alla geostatistica / Donato Posa
Autore Posa, Donato
Pubbl/distr/stampa Lecce : Adriatica editrice salentina, 1995
Descrizione fisica 123 p. : ill. ; 24 cm
Disciplina 910.015195
Soggetto topico Geology - Statistical methods
Statistics - Geology
Earth sciences - Statistical methods
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ita
Record Nr. UNISALENTO-991000582469707536
Posa, Donato  
Lecce : Adriatica editrice salentina, 1995
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Modeling uncertainty in the earth sciences [[electronic resource] /] / Jef Caers
Modeling uncertainty in the earth sciences [[electronic resource] /] / Jef Caers
Autore Caers Jef
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2011
Descrizione fisica 1 online resource (240 p.)
Disciplina 550.15118
Soggetto topico Geology - Mathematical models
Earth sciences - Statistical methods
Three-dimensional imaging in geology
Uncertainty
ISBN 1-283-17797-8
1-119-99871-9
1-119-99593-0
1-119-99592-2
9786613177971
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Modeling Uncertainty in the Earth Sciences -- Contents -- Preface -- Acknowledgements -- 1 Introduction -- 1.1 Example Application -- 1.1.1 Description -- 1.1.2 3D Modeling -- 1.2 Modeling Uncertainty -- Further Reading -- 2 Review on Statistical Analysis and Probability Theory -- 2.1 Introduction -- 2.2 Displaying Data with Graphs -- 2.2.1 Histograms -- 2.3 Describing Data with Numbers -- 2.3.1 Measuring the Center -- 2.3.2 Measuring the Spread -- 2.3.3 Standard Deviation and Variance -- 2.3.4 Properties of the Standard Deviation -- 2.3.5 Quantiles and the QQ Plot -- 2.4 Probability -- 2.4.1 Introduction -- 2.4.2 Sample Space, Event, Outcomes -- 2.4.3 Conditional Probability -- 2.4.4 Bayes' Rule -- 2.5 Random Variables -- 2.5.1 Discrete Random Variables -- 2.5.2 Continuous Random Variables -- 2.5.2.1 Probability Density Function (pdf) -- 2.5.2.2 Cumulative Distribution Function -- 2.5.3 Expectation and Variance -- 2.5.3.1 Expectation -- 2.5.3.2 Population Variance -- 2.5.4 Examples of Distribution Functions -- 2.5.4.1 The Gaussian (Normal) Random Variable and Distribution -- 2.5.4.2 Bernoulli Random Variable -- 2.5.4.3 Uniform Random Variable -- 2.5.4.4 A Poisson Random Variable -- 2.5.4.5 The Lognormal Distribution -- 2.5.5 The Empirical Distribution Function versus the Distribution Model -- 2.5.6 Constructing a Distribution Function from Data -- 2.5.7 Monte Carlo Simulation -- 2.5.8 Data Transformations -- 2.6 Bivariate Data Analysis -- 2.6.1 Introduction -- 2.6.2 Graphical Methods: Scatter plots -- 2.6.3 Data Summary: Correlation (Coefficient) -- 2.6.3.1 Definition -- 2.6.3.2 Properties of r -- Further Reading -- 3 Modeling Uncertainty: Concepts and Philosophies -- 3.1 What is Uncertainty? -- 3.2 Sources of Uncertainty -- 3.3 Deterministic Modeling -- 3.4 Models of Uncertainty -- 3.5 Model and Data Relationship.
3.6 Bayesian View on Uncertainty -- 3.7 Model Verification and Falsification -- 3.8 Model Complexity -- 3.9 Talking about Uncertainty -- 3.10 Examples -- 3.10.1 Climate Modeling -- 3.10.1.1 Description -- 3.10.1.2 Creating Data Sets Using Models -- 3.10.1.3 Parameterization of Subgrid Variability -- 3.10.1.4 Model Complexity -- 3.10.2 Reservoir Modeling -- 3.10.2.1 Description -- 3.10.2.2 Creating Data Sets Using Models -- 3.10.2.3 Parameterization of Subgrid Variability -- 3.10.2.4 Model Complexity -- Further Reading -- 4 Engineering the Earth: Making Decisions Under Uncertainty -- 4.1 Introduction -- 4.2 Making Decisions -- 4.2.1 Example Problem -- 4.2.2 The Language of Decision Making -- 4.2.3 Structuring the Decision -- 4.2.4 Modeling the Decision -- 4.2.4.1 Payoffs and Value Functions -- 4.2.4.2 Weighting -- 4.2.4.3 Trade-Offs -- 4.2.4.4 Sensitivity Analysis -- 4.3 Tools for Structuring Decision Problems -- 4.3.1 Decision Trees -- 4.3.2 Building Decision Trees -- 4.3.3 Solving Decision Trees -- 4.3.4 Sensitivity Analysis -- Further Reading -- 5 Modeling Spatial Continuity -- 5.1 Introduction -- 5.2 The Variogram -- 5.2.1 Autocorrelation in 1D -- 5.2.2 Autocorrelation in 2D and 3D -- 5.2.3 The Variogram and Covariance Function -- 5.2.4 Variogram Analysis -- 5.2.4.1 Anisotropy -- 5.2.4.2 What is the Practical Meaning of a Variogram? -- 5.2.5 A Word on Variogram Modeling -- 5.3 The Boolean or Object Model -- 5.3.1 Motivation -- 5.3.2 Object Models -- 5.4 3D Training Image Models -- Further Reading -- 6 Modeling Spatial Uncertainty -- 6.1 Introduction -- 6.2 Object-Based Simulation -- 6.3 Training Image Methods -- 6.3.1 Principle of Sequential Simulation -- 6.3.2 Sequential Simulation Based on Training Images -- 6.3.3 Example of a 3D Earth Model -- 6.4 Variogram-Based Methods -- 6.4.1 Introduction -- 6.4.2 Linear Estimation.
6.4.3 Inverse Square Distance -- 6.4.4 Ordinary Kriging -- 6.4.5 The Kriging Variance -- 6.4.6 Sequential Gaussian Simulation -- 6.4.6.1 Kriging to Create a Model of Uncertainty -- 6.4.6.2 Using Kriging to Perform (Sequential) Gaussian Simulation -- Further Reading -- 7 Constraining Spatial Models of Uncertainty with Data -- 7.1 Data Integration -- 7.2 Probability-Based Approaches -- 7.2.1 Introduction -- 7.2.2 Calibration of Information Content -- 7.2.3 Integrating Information Content -- 7.2.4 Application to Modeling Spatial Uncertainty -- 7.3 Variogram-Based Approaches -- 7.4 Inverse Modeling Approaches -- 7.4.1 Introduction -- 7.4.2 The Role of Bayes' Rule in Inverse Model Solutions -- 7.4.3 Sampling Methods -- 7.4.3.1 Rejection Sampling -- 7.4.3.2 Metropolis Sampler -- 7.4.4 Optimization Methods -- Further Reading -- 8 Modeling Structural Uncertainty -- 8.1 Introduction -- 8.2 Data for Structural Modeling in the Subsurface -- 8.3 Modeling a Geological Surface -- 8.4 Constructing a Structural Model -- 8.4.1 Geological Constraints and Consistency -- 8.4.2 Building the Structural Model -- 8.5 Gridding the Structural Model -- 8.5.1 Stratigraphic Grids -- 8.5.2 Grid Resolution -- 8.6 Modeling Surfaces through Thicknesses -- 8.7 Modeling Structural Uncertainty -- 8.7.1 Sources of Uncertainty -- 8.7.2 Models of Structural Uncertainty -- Further Reading -- 9 Visualizing Uncertainty -- 9.1 Introduction -- 9.2 The Concept of Distance -- 9.3 Visualizing Uncertainty -- 9.3.1 Distances, Metric Space and Multidimensional Scaling -- 9.3.2 Determining the Dimension of Projection -- 9.3.3 Kernels and Feature Space -- 9.3.4 Visualizing the Data-Model Relationship -- Further Reading -- 10 Modeling Response Uncertainty -- 10.1 Introduction -- 10.2 Surrogate Models and Ranking -- 10.3 Experimental Design and Response Surface Analysis -- 10.3.1 Introduction.
10.3.2 The Design of Experiments -- 10.3.3 Response Surface Designs -- 10.3.4 Simple Illustrative Example -- 10.3.5 Limitations -- 10.4 Distance Methods for Modeling Response Uncertainty -- 10.4.1 Introduction -- 10.4.2 Earth Model Selection by Clustering -- 10.4.2.1 Introduction -- 10.4.2.2 k-Means Clustering -- 10.4.2.3 Clustering of Earth Models for Response Uncertainty Evaluation -- 10.4.3 Oil Reservoir Case Study -- 10.4.4 Sensitivity Analysis -- 10.4.5 Limitations -- Further Reading -- 11 Value of Information -- 11.1 Introduction -- 11.2 The Value of Information Problem -- 11.2.1 Introduction -- 11.2.2 Reliability versus Information Content -- 11.2.3 Summary of the VOI Methodology -- 11.2.3.1 Steps 1 and 2: VOI Decision Tree -- 11.2.3.2 Steps 3 and 4: Value of Perfect Information -- 11.2.3.3 Step 5: Value of Imperfect Information -- 11.2.4 Value of Information for Earth Modeling Problems -- 11.2.5 Earth Models -- 11.2.6 Value of Information Calculation -- 11.2.7 Example Case Study -- 11.2.7.1 Introduction -- 11.2.7.2 Earth Modeling -- 11.2.7.3 Decision Problem -- 11.2.7.4 The Possible Data Sources -- 11.2.7.5 Data Interpretation -- Further Reading -- 12 Example Case Study -- 12.1 Introduction -- 12.1.1 General Description -- 12.1.2 Contaminant Transport -- 12.1.3 Costs Involved -- 12.2 Solution -- 12.2.1 Solving the Decision Problem -- 12.2.2 Buying More Data -- 12.2.2.1 Buying Geological Information -- 12.2.2.2 Buying Geophysical Information -- 12.3 Sensitivity Analysis -- Index.
Record Nr. UNINA-9910141250103321
Caers Jef  
Hoboken, N.J., : Wiley, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modeling uncertainty in the earth sciences [[electronic resource] /] / Jef Caers
Modeling uncertainty in the earth sciences [[electronic resource] /] / Jef Caers
Autore Caers Jef
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2011
Descrizione fisica 1 online resource (240 p.)
Disciplina 550.15118
Soggetto topico Geology - Mathematical models
Earth sciences - Statistical methods
Three-dimensional imaging in geology
Uncertainty
ISBN 1-283-17797-8
1-119-99871-9
1-119-99593-0
1-119-99592-2
9786613177971
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Modeling Uncertainty in the Earth Sciences -- Contents -- Preface -- Acknowledgements -- 1 Introduction -- 1.1 Example Application -- 1.1.1 Description -- 1.1.2 3D Modeling -- 1.2 Modeling Uncertainty -- Further Reading -- 2 Review on Statistical Analysis and Probability Theory -- 2.1 Introduction -- 2.2 Displaying Data with Graphs -- 2.2.1 Histograms -- 2.3 Describing Data with Numbers -- 2.3.1 Measuring the Center -- 2.3.2 Measuring the Spread -- 2.3.3 Standard Deviation and Variance -- 2.3.4 Properties of the Standard Deviation -- 2.3.5 Quantiles and the QQ Plot -- 2.4 Probability -- 2.4.1 Introduction -- 2.4.2 Sample Space, Event, Outcomes -- 2.4.3 Conditional Probability -- 2.4.4 Bayes' Rule -- 2.5 Random Variables -- 2.5.1 Discrete Random Variables -- 2.5.2 Continuous Random Variables -- 2.5.2.1 Probability Density Function (pdf) -- 2.5.2.2 Cumulative Distribution Function -- 2.5.3 Expectation and Variance -- 2.5.3.1 Expectation -- 2.5.3.2 Population Variance -- 2.5.4 Examples of Distribution Functions -- 2.5.4.1 The Gaussian (Normal) Random Variable and Distribution -- 2.5.4.2 Bernoulli Random Variable -- 2.5.4.3 Uniform Random Variable -- 2.5.4.4 A Poisson Random Variable -- 2.5.4.5 The Lognormal Distribution -- 2.5.5 The Empirical Distribution Function versus the Distribution Model -- 2.5.6 Constructing a Distribution Function from Data -- 2.5.7 Monte Carlo Simulation -- 2.5.8 Data Transformations -- 2.6 Bivariate Data Analysis -- 2.6.1 Introduction -- 2.6.2 Graphical Methods: Scatter plots -- 2.6.3 Data Summary: Correlation (Coefficient) -- 2.6.3.1 Definition -- 2.6.3.2 Properties of r -- Further Reading -- 3 Modeling Uncertainty: Concepts and Philosophies -- 3.1 What is Uncertainty? -- 3.2 Sources of Uncertainty -- 3.3 Deterministic Modeling -- 3.4 Models of Uncertainty -- 3.5 Model and Data Relationship.
3.6 Bayesian View on Uncertainty -- 3.7 Model Verification and Falsification -- 3.8 Model Complexity -- 3.9 Talking about Uncertainty -- 3.10 Examples -- 3.10.1 Climate Modeling -- 3.10.1.1 Description -- 3.10.1.2 Creating Data Sets Using Models -- 3.10.1.3 Parameterization of Subgrid Variability -- 3.10.1.4 Model Complexity -- 3.10.2 Reservoir Modeling -- 3.10.2.1 Description -- 3.10.2.2 Creating Data Sets Using Models -- 3.10.2.3 Parameterization of Subgrid Variability -- 3.10.2.4 Model Complexity -- Further Reading -- 4 Engineering the Earth: Making Decisions Under Uncertainty -- 4.1 Introduction -- 4.2 Making Decisions -- 4.2.1 Example Problem -- 4.2.2 The Language of Decision Making -- 4.2.3 Structuring the Decision -- 4.2.4 Modeling the Decision -- 4.2.4.1 Payoffs and Value Functions -- 4.2.4.2 Weighting -- 4.2.4.3 Trade-Offs -- 4.2.4.4 Sensitivity Analysis -- 4.3 Tools for Structuring Decision Problems -- 4.3.1 Decision Trees -- 4.3.2 Building Decision Trees -- 4.3.3 Solving Decision Trees -- 4.3.4 Sensitivity Analysis -- Further Reading -- 5 Modeling Spatial Continuity -- 5.1 Introduction -- 5.2 The Variogram -- 5.2.1 Autocorrelation in 1D -- 5.2.2 Autocorrelation in 2D and 3D -- 5.2.3 The Variogram and Covariance Function -- 5.2.4 Variogram Analysis -- 5.2.4.1 Anisotropy -- 5.2.4.2 What is the Practical Meaning of a Variogram? -- 5.2.5 A Word on Variogram Modeling -- 5.3 The Boolean or Object Model -- 5.3.1 Motivation -- 5.3.2 Object Models -- 5.4 3D Training Image Models -- Further Reading -- 6 Modeling Spatial Uncertainty -- 6.1 Introduction -- 6.2 Object-Based Simulation -- 6.3 Training Image Methods -- 6.3.1 Principle of Sequential Simulation -- 6.3.2 Sequential Simulation Based on Training Images -- 6.3.3 Example of a 3D Earth Model -- 6.4 Variogram-Based Methods -- 6.4.1 Introduction -- 6.4.2 Linear Estimation.
6.4.3 Inverse Square Distance -- 6.4.4 Ordinary Kriging -- 6.4.5 The Kriging Variance -- 6.4.6 Sequential Gaussian Simulation -- 6.4.6.1 Kriging to Create a Model of Uncertainty -- 6.4.6.2 Using Kriging to Perform (Sequential) Gaussian Simulation -- Further Reading -- 7 Constraining Spatial Models of Uncertainty with Data -- 7.1 Data Integration -- 7.2 Probability-Based Approaches -- 7.2.1 Introduction -- 7.2.2 Calibration of Information Content -- 7.2.3 Integrating Information Content -- 7.2.4 Application to Modeling Spatial Uncertainty -- 7.3 Variogram-Based Approaches -- 7.4 Inverse Modeling Approaches -- 7.4.1 Introduction -- 7.4.2 The Role of Bayes' Rule in Inverse Model Solutions -- 7.4.3 Sampling Methods -- 7.4.3.1 Rejection Sampling -- 7.4.3.2 Metropolis Sampler -- 7.4.4 Optimization Methods -- Further Reading -- 8 Modeling Structural Uncertainty -- 8.1 Introduction -- 8.2 Data for Structural Modeling in the Subsurface -- 8.3 Modeling a Geological Surface -- 8.4 Constructing a Structural Model -- 8.4.1 Geological Constraints and Consistency -- 8.4.2 Building the Structural Model -- 8.5 Gridding the Structural Model -- 8.5.1 Stratigraphic Grids -- 8.5.2 Grid Resolution -- 8.6 Modeling Surfaces through Thicknesses -- 8.7 Modeling Structural Uncertainty -- 8.7.1 Sources of Uncertainty -- 8.7.2 Models of Structural Uncertainty -- Further Reading -- 9 Visualizing Uncertainty -- 9.1 Introduction -- 9.2 The Concept of Distance -- 9.3 Visualizing Uncertainty -- 9.3.1 Distances, Metric Space and Multidimensional Scaling -- 9.3.2 Determining the Dimension of Projection -- 9.3.3 Kernels and Feature Space -- 9.3.4 Visualizing the Data-Model Relationship -- Further Reading -- 10 Modeling Response Uncertainty -- 10.1 Introduction -- 10.2 Surrogate Models and Ranking -- 10.3 Experimental Design and Response Surface Analysis -- 10.3.1 Introduction.
10.3.2 The Design of Experiments -- 10.3.3 Response Surface Designs -- 10.3.4 Simple Illustrative Example -- 10.3.5 Limitations -- 10.4 Distance Methods for Modeling Response Uncertainty -- 10.4.1 Introduction -- 10.4.2 Earth Model Selection by Clustering -- 10.4.2.1 Introduction -- 10.4.2.2 k-Means Clustering -- 10.4.2.3 Clustering of Earth Models for Response Uncertainty Evaluation -- 10.4.3 Oil Reservoir Case Study -- 10.4.4 Sensitivity Analysis -- 10.4.5 Limitations -- Further Reading -- 11 Value of Information -- 11.1 Introduction -- 11.2 The Value of Information Problem -- 11.2.1 Introduction -- 11.2.2 Reliability versus Information Content -- 11.2.3 Summary of the VOI Methodology -- 11.2.3.1 Steps 1 and 2: VOI Decision Tree -- 11.2.3.2 Steps 3 and 4: Value of Perfect Information -- 11.2.3.3 Step 5: Value of Imperfect Information -- 11.2.4 Value of Information for Earth Modeling Problems -- 11.2.5 Earth Models -- 11.2.6 Value of Information Calculation -- 11.2.7 Example Case Study -- 11.2.7.1 Introduction -- 11.2.7.2 Earth Modeling -- 11.2.7.3 Decision Problem -- 11.2.7.4 The Possible Data Sources -- 11.2.7.5 Data Interpretation -- Further Reading -- 12 Example Case Study -- 12.1 Introduction -- 12.1.1 General Description -- 12.1.2 Contaminant Transport -- 12.1.3 Costs Involved -- 12.2 Solution -- 12.2.1 Solving the Decision Problem -- 12.2.2 Buying More Data -- 12.2.2.1 Buying Geological Information -- 12.2.2.2 Buying Geophysical Information -- 12.3 Sensitivity Analysis -- Index.
Record Nr. UNINA-9910830595503321
Caers Jef  
Hoboken, N.J., : Wiley, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modeling uncertainty in the earth sciences / / Jef Caers
Modeling uncertainty in the earth sciences / / Jef Caers
Autore Caers Jef
Edizione [1st ed.]
Pubbl/distr/stampa Hoboken, N.J., : Wiley, 2011
Descrizione fisica 1 online resource (240 p.)
Disciplina 551.01/5195
Soggetto topico Geology - Mathematical models
Earth sciences - Statistical methods
Three-dimensional imaging in geology
Uncertainty
ISBN 9786613177971
9781283177979
1283177978
9781119998716
1119998719
9781119995937
1119995930
9781119995920
1119995922
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Modeling Uncertainty in the Earth Sciences -- Contents -- Preface -- Acknowledgements -- 1 Introduction -- 1.1 Example Application -- 1.1.1 Description -- 1.1.2 3D Modeling -- 1.2 Modeling Uncertainty -- Further Reading -- 2 Review on Statistical Analysis and Probability Theory -- 2.1 Introduction -- 2.2 Displaying Data with Graphs -- 2.2.1 Histograms -- 2.3 Describing Data with Numbers -- 2.3.1 Measuring the Center -- 2.3.2 Measuring the Spread -- 2.3.3 Standard Deviation and Variance -- 2.3.4 Properties of the Standard Deviation -- 2.3.5 Quantiles and the QQ Plot -- 2.4 Probability -- 2.4.1 Introduction -- 2.4.2 Sample Space, Event, Outcomes -- 2.4.3 Conditional Probability -- 2.4.4 Bayes' Rule -- 2.5 Random Variables -- 2.5.1 Discrete Random Variables -- 2.5.2 Continuous Random Variables -- 2.5.2.1 Probability Density Function (pdf) -- 2.5.2.2 Cumulative Distribution Function -- 2.5.3 Expectation and Variance -- 2.5.3.1 Expectation -- 2.5.3.2 Population Variance -- 2.5.4 Examples of Distribution Functions -- 2.5.4.1 The Gaussian (Normal) Random Variable and Distribution -- 2.5.4.2 Bernoulli Random Variable -- 2.5.4.3 Uniform Random Variable -- 2.5.4.4 A Poisson Random Variable -- 2.5.4.5 The Lognormal Distribution -- 2.5.5 The Empirical Distribution Function versus the Distribution Model -- 2.5.6 Constructing a Distribution Function from Data -- 2.5.7 Monte Carlo Simulation -- 2.5.8 Data Transformations -- 2.6 Bivariate Data Analysis -- 2.6.1 Introduction -- 2.6.2 Graphical Methods: Scatter plots -- 2.6.3 Data Summary: Correlation (Coefficient) -- 2.6.3.1 Definition -- 2.6.3.2 Properties of r -- Further Reading -- 3 Modeling Uncertainty: Concepts and Philosophies -- 3.1 What is Uncertainty? -- 3.2 Sources of Uncertainty -- 3.3 Deterministic Modeling -- 3.4 Models of Uncertainty -- 3.5 Model and Data Relationship.
3.6 Bayesian View on Uncertainty -- 3.7 Model Verification and Falsification -- 3.8 Model Complexity -- 3.9 Talking about Uncertainty -- 3.10 Examples -- 3.10.1 Climate Modeling -- 3.10.1.1 Description -- 3.10.1.2 Creating Data Sets Using Models -- 3.10.1.3 Parameterization of Subgrid Variability -- 3.10.1.4 Model Complexity -- 3.10.2 Reservoir Modeling -- 3.10.2.1 Description -- 3.10.2.2 Creating Data Sets Using Models -- 3.10.2.3 Parameterization of Subgrid Variability -- 3.10.2.4 Model Complexity -- Further Reading -- 4 Engineering the Earth: Making Decisions Under Uncertainty -- 4.1 Introduction -- 4.2 Making Decisions -- 4.2.1 Example Problem -- 4.2.2 The Language of Decision Making -- 4.2.3 Structuring the Decision -- 4.2.4 Modeling the Decision -- 4.2.4.1 Payoffs and Value Functions -- 4.2.4.2 Weighting -- 4.2.4.3 Trade-Offs -- 4.2.4.4 Sensitivity Analysis -- 4.3 Tools for Structuring Decision Problems -- 4.3.1 Decision Trees -- 4.3.2 Building Decision Trees -- 4.3.3 Solving Decision Trees -- 4.3.4 Sensitivity Analysis -- Further Reading -- 5 Modeling Spatial Continuity -- 5.1 Introduction -- 5.2 The Variogram -- 5.2.1 Autocorrelation in 1D -- 5.2.2 Autocorrelation in 2D and 3D -- 5.2.3 The Variogram and Covariance Function -- 5.2.4 Variogram Analysis -- 5.2.4.1 Anisotropy -- 5.2.4.2 What is the Practical Meaning of a Variogram? -- 5.2.5 A Word on Variogram Modeling -- 5.3 The Boolean or Object Model -- 5.3.1 Motivation -- 5.3.2 Object Models -- 5.4 3D Training Image Models -- Further Reading -- 6 Modeling Spatial Uncertainty -- 6.1 Introduction -- 6.2 Object-Based Simulation -- 6.3 Training Image Methods -- 6.3.1 Principle of Sequential Simulation -- 6.3.2 Sequential Simulation Based on Training Images -- 6.3.3 Example of a 3D Earth Model -- 6.4 Variogram-Based Methods -- 6.4.1 Introduction -- 6.4.2 Linear Estimation.
6.4.3 Inverse Square Distance -- 6.4.4 Ordinary Kriging -- 6.4.5 The Kriging Variance -- 6.4.6 Sequential Gaussian Simulation -- 6.4.6.1 Kriging to Create a Model of Uncertainty -- 6.4.6.2 Using Kriging to Perform (Sequential) Gaussian Simulation -- Further Reading -- 7 Constraining Spatial Models of Uncertainty with Data -- 7.1 Data Integration -- 7.2 Probability-Based Approaches -- 7.2.1 Introduction -- 7.2.2 Calibration of Information Content -- 7.2.3 Integrating Information Content -- 7.2.4 Application to Modeling Spatial Uncertainty -- 7.3 Variogram-Based Approaches -- 7.4 Inverse Modeling Approaches -- 7.4.1 Introduction -- 7.4.2 The Role of Bayes' Rule in Inverse Model Solutions -- 7.4.3 Sampling Methods -- 7.4.3.1 Rejection Sampling -- 7.4.3.2 Metropolis Sampler -- 7.4.4 Optimization Methods -- Further Reading -- 8 Modeling Structural Uncertainty -- 8.1 Introduction -- 8.2 Data for Structural Modeling in the Subsurface -- 8.3 Modeling a Geological Surface -- 8.4 Constructing a Structural Model -- 8.4.1 Geological Constraints and Consistency -- 8.4.2 Building the Structural Model -- 8.5 Gridding the Structural Model -- 8.5.1 Stratigraphic Grids -- 8.5.2 Grid Resolution -- 8.6 Modeling Surfaces through Thicknesses -- 8.7 Modeling Structural Uncertainty -- 8.7.1 Sources of Uncertainty -- 8.7.2 Models of Structural Uncertainty -- Further Reading -- 9 Visualizing Uncertainty -- 9.1 Introduction -- 9.2 The Concept of Distance -- 9.3 Visualizing Uncertainty -- 9.3.1 Distances, Metric Space and Multidimensional Scaling -- 9.3.2 Determining the Dimension of Projection -- 9.3.3 Kernels and Feature Space -- 9.3.4 Visualizing the Data-Model Relationship -- Further Reading -- 10 Modeling Response Uncertainty -- 10.1 Introduction -- 10.2 Surrogate Models and Ranking -- 10.3 Experimental Design and Response Surface Analysis -- 10.3.1 Introduction.
10.3.2 The Design of Experiments -- 10.3.3 Response Surface Designs -- 10.3.4 Simple Illustrative Example -- 10.3.5 Limitations -- 10.4 Distance Methods for Modeling Response Uncertainty -- 10.4.1 Introduction -- 10.4.2 Earth Model Selection by Clustering -- 10.4.2.1 Introduction -- 10.4.2.2 k-Means Clustering -- 10.4.2.3 Clustering of Earth Models for Response Uncertainty Evaluation -- 10.4.3 Oil Reservoir Case Study -- 10.4.4 Sensitivity Analysis -- 10.4.5 Limitations -- Further Reading -- 11 Value of Information -- 11.1 Introduction -- 11.2 The Value of Information Problem -- 11.2.1 Introduction -- 11.2.2 Reliability versus Information Content -- 11.2.3 Summary of the VOI Methodology -- 11.2.3.1 Steps 1 and 2: VOI Decision Tree -- 11.2.3.2 Steps 3 and 4: Value of Perfect Information -- 11.2.3.3 Step 5: Value of Imperfect Information -- 11.2.4 Value of Information for Earth Modeling Problems -- 11.2.5 Earth Models -- 11.2.6 Value of Information Calculation -- 11.2.7 Example Case Study -- 11.2.7.1 Introduction -- 11.2.7.2 Earth Modeling -- 11.2.7.3 Decision Problem -- 11.2.7.4 The Possible Data Sources -- 11.2.7.5 Data Interpretation -- Further Reading -- 12 Example Case Study -- 12.1 Introduction -- 12.1.1 General Description -- 12.1.2 Contaminant Transport -- 12.1.3 Costs Involved -- 12.2 Solution -- 12.2.1 Solving the Decision Problem -- 12.2.2 Buying More Data -- 12.2.2.1 Buying Geological Information -- 12.2.2.2 Buying Geophysical Information -- 12.3 Sensitivity Analysis -- Index.
Record Nr. UNINA-9911019771503321
Caers Jef  
Hoboken, N.J., : Wiley, 2011
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Modern Spatiotemporal Geostatistics
Modern Spatiotemporal Geostatistics
Autore Christakos George
Edizione [1st ed.]
Pubbl/distr/stampa Newburyport, : Dover Publications, 2013
Descrizione fisica 1 online resource (593 p.)
Disciplina 550.72/7
Collana Dover Earth Science
Soggetto topico Earth sciences - Statistical methods
Maximum entropy method
Bayesian statistical decision theory
Geology
Earth & Environmental Sciences
Geology - General
ISBN 9781523132010
1523132019
9780486310930
0486310930
9781680150940
1680150944
Classificazione SCI031000
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright Page; Dedication; Preface; Contents; Mapping Fundamentals; The Epistemic Status of Modern Spatiotemporal Geostatistics: It Pays to Theorize!; Why Modern Geostatistics?; Indetermination thesis; Spatiotemporal geometry; Sources of physical knowledge; The non-Procrustean spirit; Bayesian Maximum Entropy Space/Time Analysis and Mapping; BME features; The Integration Capability of Modern Spatiotemporal Geostatistics; The "Knowledge-Map" Approach; Scientific content; 1. Spatiotemporal Mapping in Natural Sciences; A More Realistic Concept
The Spatiotemporal Continuum IdeaThe Coordinate System; Euclidean coordinate systems; Non-Euclidean coordinate systems; Metrical Structure; Separate metrical structures; Composite metrical structures; Some comments on physical spatiotemporal geometry; The Field Idea; Restrictions on spatiotemporal geometry imposed by field measurements and natural media; Restrictions on spatiotemporal geometry imposed by physical laws; The Complementarity Idea; Putting Things Together: The Spatiotemporal Random Field Concept; Correlation analysis and spatiotemporal geometry
Permissibility criteria and spatiotemporal geometryEffect of spatiotemporal geometry on mapping; Some Final Thoughts; 2. Spatiotemporal Geometry; From the General to the Specific; The General Knowledge Base; A mathematical formulation of the general knowledge base; General knowledge in terms of statistical moments; General knowledge in terms of physical laws; Some other forms of general knowledge; The Specificatory Knowledge Base; Specificatory knowledge in terms of hard data; Specificatory knowledge in terms of soft data; Summa Theologica; 3. Physical Knowledge
Acquisition and Processing of Physical KnowledgeEpistemic Geostatistics and the BME Analysis; Prior stage; Meta-prior stage; Integration or posterior stage; Conditional Probability of a Spatiotemporal Map and its Relation to the Probability of Conditionals; Material and strict map conditionals; Other map conditionals; The BME Net; 4. The Epistemic Paradigm; A Pragmatic Framework of the Mapping Problem; The Prior Stage; Map information measures in light of general knowledge; General knowledge-based map pdf
General knowledge in the form of random field statistics (including multiple-point statistics)General knowledge in the form of physical laws; Possible modifications and generalizations of the prior stage; The Meta-Prior Stage; The Integration or Posterior Stage; The Structure of the Modern Spatiotemporal Geostatistics Paradigm; The Two Legs on Which the BME Equations Stand; 5. Mathematical Formulation of the BME Method; Specificatory Knowledge and Single-Point Mapping; Posterior Operators for Interval and Probabilistic Soft Data; Posterior Operators for Other Forms of Soft Data; Discussion
6. Analytical Expressions of the Posterior Operator
Record Nr. UNINA-9911006601103321
Christakos George  
Newburyport, : Dover Publications, 2013
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modern spatiotemporal geostatistics [[electronic resource] /] / George Christakos
Modern spatiotemporal geostatistics [[electronic resource] /] / George Christakos
Autore Christakos George
Pubbl/distr/stampa Oxford [England] ; ; New York, : Oxford University Press, 2000
Descrizione fisica 1 online resource (307 p.)
Disciplina 550.15195
550/.7/27
Collana International Association for Mathematical Geology studies in mathematical geology
Soggetto topico Bayesian statistical decision theory
Earth sciences - Statistical methods
Maximum entropy method
Soggetto genere / forma Electronic books.
ISBN 1-280-83477-3
9786610834778
0-19-803179-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE; TABLE OF CONTENTS; CHAPTER 1: Spatiotemporal Mapping in Natural Sciences; CHAPTER 2: Spatiotemporal Geometry; CHAPTER 3: Physical Knowledge; CHAPTER 4: The Epistemic Paradigm; CHAPTER 5: Mathematical Formulation of the BME Method; CHAPTER 6: Analytical Expressions of the Posterior Operator; CHAPTER 7: The Choice of a Spatiotemporal Estimate; CHAPTER 8: Uncertainty Assessment; CHAPTER 9: Modifications of Formal BME Analysis; CHAPTER 10: Single-Point Analytical Formulations; CHAPTER 11: Multipoint Analytical Formulations
CHAPTER 12: Popular Methods in the Light of Modern Spatiotemporal GeostatisticsCHAPTER 13: A Call Not to Arms but to Research; BIBLIOGRAPHY; INDEX
Record Nr. UNINA-9910453620103321
Christakos George  
Oxford [England] ; ; New York, : Oxford University Press, 2000
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Modern spatiotemporal geostatistics [[electronic resource] /] / George Christakos
Modern spatiotemporal geostatistics [[electronic resource] /] / George Christakos
Autore Christakos George
Pubbl/distr/stampa Oxford [England] ; ; New York, : Oxford University Press, 2000
Descrizione fisica 1 online resource (307 p.)
Disciplina 550.15195
550/.7/27
Collana International Association for Mathematical Geology studies in mathematical geology
Soggetto topico Bayesian statistical decision theory
Earth sciences - Statistical methods
Maximum entropy method
ISBN 1-280-83477-3
9786610834778
0-19-803179-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto PREFACE; TABLE OF CONTENTS; CHAPTER 1: Spatiotemporal Mapping in Natural Sciences; CHAPTER 2: Spatiotemporal Geometry; CHAPTER 3: Physical Knowledge; CHAPTER 4: The Epistemic Paradigm; CHAPTER 5: Mathematical Formulation of the BME Method; CHAPTER 6: Analytical Expressions of the Posterior Operator; CHAPTER 7: The Choice of a Spatiotemporal Estimate; CHAPTER 8: Uncertainty Assessment; CHAPTER 9: Modifications of Formal BME Analysis; CHAPTER 10: Single-Point Analytical Formulations; CHAPTER 11: Multipoint Analytical Formulations
CHAPTER 12: Popular Methods in the Light of Modern Spatiotemporal GeostatisticsCHAPTER 13: A Call Not to Arms but to Research; BIBLIOGRAPHY; INDEX
Record Nr. UNINA-9910782204303321
Christakos George  
Oxford [England] ; ; New York, : Oxford University Press, 2000
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui