Muutke küpsiste eelistusi

E-raamat: Modeling Uncertainty in the Earth Sciences [Wiley Online]

(Stanford University)
  • Formaat: 246 pages
  • Ilmumisaeg: 24-Jun-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119995922
  • ISBN-13: 9781119995920
Teised raamatud teemal:
  • Wiley Online
  • Hind: 95,16 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Modeling Uncertainty in the Earth SciencesSuurem pilt
  • Formaat: 246 pages
  • Ilmumisaeg: 24-Jun-2011
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1119995922
  • ISBN-13: 9781119995920
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119995920
Modeling Uncertainty in the Earth Sciences highlights the various issues, techniques and practical modeling tools available for modeling the uncertainty of complex Earth systems and the impact that it has on practical situations. The aim of the book is to provide an introductory overview which covers a broad range of tried-and-tested tools. Descriptions of concepts, philosophies, challenges, methodologies and workflows give the reader an understanding of the best way to make decisions under uncertainty for Earth Science problems.

The book covers key issues such as: Spatial and time aspect; large complexity and dimensionality; computation power; costs of 'engineering' the Earth; uncertainty in the modeling and decision process. Focusing on reliable and practical methods this book provides an invaluable primer for the complex area of decision making with uncertainty in the Earth Sciences.

Preface xi
Acknowledgements xvii
1 Introduction
1(8)
1.1 Example Application
1(3)
1.1.1 Description
1(2)
1.1.2 3D Modeling
3(1)
1.2 Modeling Uncertainty
4(5)
Further Reading
8(1)
2 Review on Statistical Analysis and Probability Theory
9(30)
2.1 Introduction
9(1)
2.2 Displaying Data with Graphs
10(3)
2.2.1 Histograms
10(3)
2.3 Describing Data with Numbers
13(3)
2.3.1 Measuring the Center
13(1)
2.3.2 Measuring the Spread
14(1)
2.3.3 Standard Deviation and Variance
14(1)
2.3.4 Properties of the Standard Deviation
15(1)
2.3.5 Quantiles and the QQ Plot
15(1)
2.4 Probability
16(5)
2.4.1 Introduction
16(1)
2.4.2 Sample Space, Event, Outcomes
17(1)
2.4.3 Conditional Probability
18(1)
2.4.4 Bayes' Rule
19(2)
2.5 Random Variables
21(12)
2.5.1 Discrete Random Variables
21(1)
2.5.2 Continuous Random Variables
21(1)
2.5.2.1 Probability Density Function (pdf)
21(1)
2.5.2.2 Cumulative Distribution Function
22(1)
2.5.3 Expectation and Variance
23(1)
2.5.3.1 Expectation
23(1)
2.5.3.2 Population Variance
24(1)
2.5.4 Examples of Distribution Functions
24(1)
2.5.4.1 The Gaussian (Normal) Random Variable and Distribution
24(1)
2.5.4.2 Bernoulli Random Variable
25(1)
2.5.4.3 Uniform Random Variable
26(1)
2.5.4.4 A Poisson Random Variable
26(1)
2.5.4.5 The Lognormal Distribution
27(1)
2.5.5 The Empirical Distribution Function versus the Distribution Model
28(1)
2.5.6 Constructing a Distribution Function from Data
29(1)
2.5.7 Monte Carlo Simulation
30(2)
2.5.8 Data Transformations
32(1)
2.6 Bivariate Data Analysis
33(6)
2.6.1 Introduction
33(1)
2.6.2 Graphical Methods: Scatter plots
33(2)
2.6.3 Data Summary: Correlation (Coefficient)
35(1)
2.6.3.1 Definition
35(2)
2.6.3.2 Properties of r
37(1)
Further Reading
37(2)
3 Modeling Uncertainty: Concepts and Philosophies
39(16)
3.1 What is Uncertainty?
39(1)
3.2 Sources of Uncertainty
40(1)
3.3 Deterministic Modeling
41(2)
3.4 Models of Uncertainty
43(1)
3.5 Model and Data Relationship
44(1)
3.6 Bayesian View on Uncertainty
45(3)
3.7 Model Verification and Falsification
48(1)
3.8 Model Complexity
49(1)
3.9 Talking about Uncertainty
50(1)
3.10 Examples
51(4)
3.10.1 Climate Modeling
51(1)
3.10.1.1 Description
51(1)
3.10.1.2 Creating Data Sets Using Models
51(1)
3.10.1.3 Parameterization of Subgrid Variability
52(1)
3.10.1.4 Model Complexity
52(1)
3.10.2 Reservoir Modeling
52(1)
3.10.2.1 Description
52(1)
3.10.2.2 Creating Data Sets Using Models
53(1)
3.10.2.3 Parameterization of Subgrid Variability
53(1)
3.10.2.4 Model Complexity
54(1)
Further Reading
54(1)
4 Engineering the Earth: Making Decisions Under Uncertainty
55(22)
4.1 Introduction
55(2)
4.2 Making Decisions
57(13)
4.2.1 Example Problem
57(2)
4.2.2 The Language of Decision Making
59(1)
4.2.3 Structuring the Decision
60(1)
4.2.4 Modeling the Decision
61(1)
4.2.4.1 Payoffs and Value Functions
62(1)
4.2.4.2 Weighting
63(2)
4.2.4.3 Trade-Offs
65(2)
4.2.4.4 Sensitivity Analysis
67(3)
4.3 Tools for Structuring Decision Problems
70(7)
4.3.1 Decision Trees
70(1)
4.3.2 Building Decision Trees
70(2)
4.3.3 Solving Decision Trees
72(4)
4.3.4 Sensitivity Analysis
76(1)
Further Reading
76(1)
5 Modeling Spatial Continuity
77(16)
5.1 Introduction
77(2)
5.2 The Variogram
79(8)
5.2.1 Autocorrelation in 1D
79(3)
5.2.2 Autocorrelation in 2D and 3D
82(2)
5.2.3 The Variogram and Covariance Function
84(2)
5.2.4 Variogram Analysis
86(1)
5.2.4.1 Anisotropy
86(1)
5.2.4.2 What is the Practical Meaning of a Variogram?
87(1)
5.2.5 A Word on Variogram Modeling
87(1)
5.3 The Boolean or Object Model
87(3)
5.3.1 Motivation
87(2)
5.3.2 Object Models
89(1)
5.4 3D Training Image Models
90(3)
Further Reading
92(1)
6 Modeling Spatial Uncertainty
93(14)
6.1 Introduction
93(1)
6.2 Object-Based Simulation
94(2)
6.3 Training Image Methods
96(4)
6.3.1 Principle of Sequential Simulation
96(2)
6.3.2 Sequential Simulation Based on Training Images
98(1)
6.3.3 Example of a 3D Earth Model
99(1)
6.4 Variogram-Based Methods
100(7)
6.4.1 Introduction
100(1)
6.4.2 Linear Estimation
101(1)
6.4.3 Inverse Square Distance
102(1)
6.4.4 Ordinary Kriging
103(1)
6.4.5 The Kriging Variance
104(1)
6.4.6 Sequential Gaussian Simulation
104(1)
6.4.6.1 Kriging to Create a Model of Uncertainty
104(1)
6.4.6.2 Using Kriging to Perform (Sequential) Gaussian Simulation
104(2)
Further Reading
106(1)
7 Constraining Spatial Models of Uncertainty with Data
107(26)
7.1 Data Integration
107(1)
7.2 Probability-Based Approaches
108(6)
7.2.1 Introduction
108(1)
7.2.2 Calibration of Information Content
109(1)
7.2.3 Integrating Information Content
110(3)
7.2.4 Application to Modeling Spatial Uncertainty
113(1)
7.3 Variogram-Based Approaches
114(2)
7.4 Inverse Modeling Approaches
116(17)
7.4.1 Introduction
116(2)
7.4.2 The Role of Bayes' Rule in Inverse Model Solutions
118(7)
7.4.3 Sampling Methods
125(1)
7.4.3.1 Rejection Sampling
125(3)
7.4.3.2 Metropolis Sampler
128(2)
7.4.4 Optimization Methods
130(1)
Further Reading
131(2)
8 Modeling Structural Uncertainty
133(20)
8.1 Introduction
133(2)
8.2 Data for Structural Modeling in the Subsurface
135(1)
8.3 Modeling a Geological Surface
136(2)
8.4 Constructing a Structural Model
138(3)
8.4.1 Geological Constraints and Consistency
138(2)
8.4.2 Building the Structural Model
140(1)
8.5 Gridding the Structural Model
141(3)
8.5.1 Stratigraphic Grids
141(1)
8.5.2 Grid Resolution
142(2)
8.6 Modeling Surfaces through Thicknesses
144(1)
8.7 Modeling Structural Uncertainty
144(9)
8.7.1 Sources of Uncertainty
146(3)
8.7.2 Models of Structural Uncertainty
149(2)
Further Reading
151(2)
9 Visualizing Uncertainty
153(18)
9.1 Introduction
153(1)
9.2 The Concept of Distance
154(2)
9.3 Visualizing Uncertainty
156(15)
9.3.1 Distances, Metric Space and Multidimensional Scaling
156(6)
9.3.2 Determining the Dimension of Projection
162(1)
9.3.3 Kernels and Feature Space
163(3)
9.3.4 Visualizing the Data-Model Relationship
166(4)
Further Reading
170(1)
10 Modeling Response Uncertainty
171(22)
10.1 Introduction
171(1)
10.2 Surrogate Models and Ranking
172(1)
10.3 Experimental Design and Response Surface Analysis
173(8)
10.3.1 Introduction
173(1)
10.3.2 The Design of Experiments
173(3)
10.3.3 Response Surface Designs
176(1)
10.3.4 Simple Illustrative Example
177(2)
10.3.5 Limitations
179(2)
10.4 Distance Methods for Modeling Response Uncertainty
181(12)
10.4.1 Introduction
181(1)
10.4.2 Earth Model Selection by Clustering
182(1)
10.4.2.1 Introduction
182(1)
10.4.2.2 k-Means Clustering
183(2)
10.4.2.3 Clustering of Earth Models for Response Uncertainty Evaluation
185(1)
10.4.3 Oil Reservoir Case Study
186(2)
10.4.4 Sensitivity Analysis
188(3)
10.4.5 Limitations
191(1)
Further Reading
191(2)
11 Value of Information
193(22)
11.1 Introduction
193(1)
11.2 The Value of Information Problem
194(21)
11.2.1 Introduction
194(1)
11.2.2 Reliability versus Information Content
195(1)
11.2.3 Summary of the VOI Methodology
196(1)
11.2.3.1 Steps 1 and 2: VOI Decision Tree
197(1)
11.2.3.2 Steps 3 and 4: Value of Perfect Information
198(3)
11.2.3.3 Step 5: Value of Imperfect Information
201(1)
11.2.4 Value of Information for Earth Modeling Problems
202(1)
11.2.5 Earth Models
202(1)
11.2.6 Value of Information Calculation
203(5)
11.2.7 Example Case Study
208(1)
11.2.7.1 Introduction
208(1)
11.2.7.2 Earth Modeling
208(1)
11.2.7.3 Decision Problem
209(1)
11.2.7.4 The Possible Data Sources
210(1)
11.2.7.5 Data Interpretation
211(2)
Further Reading
213(2)
12 Example Case Study
215(10)
12.1 Introduction
215(3)
12.1.1 General Description
215(3)
12.1.2 Contaminant Transport
218(1)
12.1.3 Costs Involved
218(1)
12.2 Solution
218(3)
12.2.1 Solving the Decision Problem
218(1)
12.2.2 Buying More Data
219(1)
12.2.2.1 Buying Geological Information
219(2)
12.2.2.2 Buying Geophysical Information
221(1)
12.3 Sensitivity Analysis
221(4)
Index 225
Jef Caers, Associate Professor of Energy Resources Engineering, Department of Energy Resources Engineering, Stanford University, Stanford, CA.
Püsilink: https://www.kriso.ee/db/9781119995920_pe.html
Märksõnad: