Sen Spatial Modeling Principles in Earth Sciences

1. Auflage 2009
ISBN: 978-1-4020-9672-3
Verlag: Springer Netherlands
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 351 Seiten

Spatial Modeling Principles in Earth Sciences presents fundamentals of spatial data analysis used in hydrology, geology, meteorology, atmospheric science and related fields. It examines methods for the quantitative determination of the spatial distribution patterns. This book brings together the material from the current literature in earth sciences and practical examples. It provides a sound background of philosophical, logical, rational and physical principles of spatial data and analysis, and explains how it can be modeled and applied in earth sciences projects and designs. It collects information not previously available in one source, and provides methodology for the treatment of spatial data to find the most rational and practical solution. The book is a valuable resource for students, researchers and practitioners of a broad range of disciplines including geology, geography, hydrology, meteorology, environment, image processing, spatial modeling and related topics.

Prof. Dr. Zekai Sen is a researcher at the Istanbul Technical University, Turkey. His main interests are renewable energy (especially solar energy), hydrology, water resources, hydrogeology, hydrometeorology, hydraulics, philosophy of science, and science history. He has been appointed by the United Nations as a member of the Intergovernmental Panel on Climate Change (IPCC) for research on the effects of climate change. He published more than 200 papers in about 50 scientific journals, and 3 books: Applied Hydrogeology for Scientists and Engineers (1995, CRC Lewis Publishers), Wadi Hydrology (2008, CRC Lewis Publishers), and Solar Energy Fundamentals and Modeling Techniques: Atmosphere, Environment, Climate Change and Renewable Energy (2008, Springer).

Autoren/Hrsg.

Sen, Zekai

Inhaltsverzeichnis

1;Preface;6
2;Contents;8
3;1 Introduction;11
3.1;1.1 General;11
3.2;1.2 Earth Sciences Phenomena;12
3.3;1.3 Variability;17
3.4;1.4 Determinism Versus Uncertainty;22
3.5;1.5 Earth, Environment, and Atmospheric Researches;26
3.6;1.6 Random Field (RF);27
3.7;1.7 Regionalized Variable (ReV);28
3.8;References;29
4;2 Data Types and Logical Processing Methods;30
4.1;2.1 General;30
4.2;2.2 Observations;31
4.3;2.3 Numerical Data Types;34
4.4;2.4 Sampling;36
4.5;2.5 Number of Data;40
4.5.1;2.5.1 Small Sample Length of Independent Models;42
4.5.2;2.5.2 Small Sample Length of Dependent Models;44
4.6;2.6 Regional Representation;50
4.6.1;2.6.1 Variability Range;51
4.6.2;2.6.2 Inverse Distance Models;54
4.7;2.7 Sub-areal Partition;55
4.7.1;2.7.1 Triangularization;56
4.8;2.8 Polygonizations;60
4.8.1;2.8.1 Delaney, Varoni, and Thiessen Polygons;61
4.8.2;2.8.2 Percentage-Weighted Polygon (PWP) Method;64
4.9;2.9 Areal Coverage Probability;76
4.9.1;2.9.1 Theoretical Treatment;78
4.9.2;2.9.2 Extreme Value Probabilities;81
4.10;2.10 Spatio-Temporal Drought Theory and Analysis;82
4.10.1;2.10.1 Drought Parameters;85
4.11;References;90
5;3 Classical Spatial Variation Models;92
5.1;3.1 General;92
5.2;3.2 Spatio-Temporal Characteristics;92
5.3;3.3 Spatial Pattern Search;93
5.4;3.4 Spatial Data Analysis Needs;95
5.5;3.5 Simple Uniformity Test;102
5.6;3.6 Random Field;104
5.7;3.7 Cluster Sampling;107
5.8;3.8 Nearest Neighbor Analysis;108
5.9;3.9 Search Algorithms;111
5.9.1;3.9.1 Geometric Weighting Functions;112
5.10;3.10 Trend Surface Analysis;115
5.10.1;3.10.1 Trend Model Parameter Estimations;117
5.11;3.11 Multisite Kalman Filter Methodology;118
5.11.1;3.11.1 One-Dimensional Kalman Filter;121
5.11.2;3.11.2 Kalman Filter Application;124
5.12;References;135
6;4 Spatial Dependence Measures;136
6.1;4.1 General;136
6.2;4.2 Isotropy, Anisotropy, and Homogeneity;138
6.3;4.3 Spatial Dependence Function;141
6.4;4.4 Spatial Correlation Function;144
6.4.1;4.4.1 Correlation Coefficient Drawback;145
6.5;4.5 Semivariogram Regional Dependence Measure;149
6.5.1;4.5.1 SV Philosophy;149
6.5.2;4.5.2 SV Definition;153
6.5.3;4.5.3 SV Limitations;158
6.6;4.6 Sample SV;159
6.7;4.7 Theoretical SV;162
6.7.1;4.7.1 Simple Nugget SV;165
6.7.2;4.7.2 Linear SV;166
6.7.3;4.7.3 Exponential SV;168
6.7.4;4.7.4 Gaussian SV;168
6.7.5;4.7.5 Quadratic SV;169
6.7.6;4.7.6 Rational Quadratic SV;169
6.7.7;4.7.7 Power SV;170
6.7.8;4.7.8 Wave (Hole Effect) SV;171
6.7.9;4.7.9 Spherical SV;171
6.7.10;4.7.10 Logarithmic SV;172
6.8;4.8 Cumulative Semivariogram;173
6.8.1;4.8.1 Sample CSV;176
6.8.2;4.8.2 Theoretical CSV Models;178
6.8.2.1;4.8.2.1 Linear Model;178
6.8.2.2;4.8.2.2 Power Model;180
6.8.2.3;4.8.2.3 Exponential CSV;181
6.8.2.4;4.8.2.4 Logarithmic CSV;182
6.8.2.5;4.8.2.5 Gaussian CSV;183
6.9;4.9 Point Cumulative Semivariogram;184
6.10;4.10 Spatial Dependence Function;190
6.11;References;208
7;5 Spatial Modeling;211
7.1;5.1 General;212
7.2;5.2 Spatial Estimation of ReV;213
7.3;5.3 Optimum Interpolation Model;215
7.3.1;5.3.1 Data and Application;219
7.3.1.1;5.3.1.1 Spatial Correlation Function;223
7.3.1.2;5.3.1.2 Expected Error;226
7.3.1.3;5.3.1.3 Data Search and Selection Procedure;227
7.3.1.4;5.3.1.4 Cross-Validation of the Model;230
7.4;5.4 Geostatistical Analysis;231
7.4.1;5.4.1 Kriging Technique;233
7.4.1.1;5.4.1.1 Intrinsic Property;234
7.5;5.5 Geostatistical Estimator (Kriging);236
7.5.1;5.5.1 Kriging Methodologies and Advantages;238
7.6;5.6 Simple Kriging;240
7.7;5.7 Ordinary Kriging;247
7.8;5.8 Universal Kriging;253
7.9;5.9 Block Kriging;256
7.10;5.10 Triple Diagram Model;257
7.11;5.11 Regional Rainfall Pattern Description;264
7.12;References;274
8;6 Spatial Simulation;278
8.1;6.1 General;278
8.2;6.2 3D Autoregressive Model;280
8.2.1;6.2.1 Parameters Estimation;281
8.2.2;6.2.2 2D Uniform Model Parameters;283
8.2.3;6.2.3 Extension to 3D;286
8.3;6.3 Rock Quality Designation Simulation;288
8.3.1;6.3.1 Independent Intact Lengths;288
8.3.2;6.3.2 Dependent Intact Lengths;297
8.3.2.1;6.3.2.1 Correlation Measurement;299
8.3.2.2;6.3.2.2 RQD Formulation and Discussion;300
8.3.2.3;6.3.2.3 Applications;306
8.4;6.4 RQD and Correlated Intact Length Simulation;307
8.4.1;6.4.1 Proposed Models of Persistance;310
8.4.1.1;6.4.1.1 The Independent Process;310
8.4.1.2;6.4.1.2 First-Order Markov Process;311
8.4.1.3;6.4.1.3 ARIMA (1, 1) Process;312
8.4.2;6.4.2 Simulation of Intact Lengths;312
8.5;6.5 Autorun Simulation of Porous Material;317
8.5.1;6.5.1 Line Characteristic Function of Porous Medium;319
8.5.2;6.5.2 Autorun Analysis of Sandstone;319
8.5.3;6.5.3 Autorun Modeling of Porous Media;323
8.6;6.6 CSV Technique for Identification of Intact Length Correlation Structure;328
8.6.1;6.6.1 Intact Length CSV;330
8.6.2;6.6.2 Theoretical CSV Model;331
8.7;6.7 Multidirectional RQD Simulation;340
8.7.1;6.7.1 Fracture Network Model;341
8.7.2;6.7.2 RQD Analysis;342
8.7.3;6.7.3 RQD Simulation Results;345
9;References;347