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Multiple-point Geostatistics : Stochastic Modeling with Training Images.
This book provides a comprehensive introduction to multiple-point geostatistics, where spatial continuity is described using training images. Multiple-point geostatistics aims at bridging the gap between physical modelling/realism and spatio-temporal stochastic modelling. The book provides an overvi...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2014.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Multiple-point geostatistics; Contents; Preface; Acknowledgments; Part I Concepts; 1 Hiking in the Sierra Nevada; 1.1 An imaginary outdoor adventure company: Buena Sierra; 1.2 What lies ahead; 2 Spatial estimation based on random function theory; 2.1 Assumptions of stationarity; 2.2 Assumption of stationarity in spatial problems; 2.3 The kriging solution; 2.3.1 Unbiasedness condition; 2.3.2 Minimizing squared loss; 2.4 Estimating covariances; 2.5 Semivariogram modeling; 2.6 Using a limited neighborhood; 2.7 Universal kriging; 2.8 Semivariogram modeling for universal kriging.
- 2.9 Simple trend example case2.10 Nonstationary covariances; 2.11 Assessment; References; 3 Universal kriging with training images; 3.1 Choosing for random function theory or not?; 3.2 Formulation of universal kriging with training images; 3.2.1 Zero error-sum condition; 3.2.2 Minimum sum of square error condition; 3.3 Positive definiteness of the sop matrix; 3.4 Simple kriging with training images; 3.5 Creating a map of estimates; 3.6 Effect of the size of the training image; 3.7 Effect of the nature of the training image; 3.8 Training images for nonstationary modeling.
- 3.9 Spatial estimation with nonstationary training images3.10 Summary of methodological differences; References; 4 Stochastic simulations based on random function theory; 4.1 The goal of stochastic simulations; 4.2 Stochastic simulation: Gaussian theory; 4.3 The sequential Gaussian simulation algorithm; 4.4 Properties of multi-Gaussian realizations; 4.5 Beyond Gaussian or beyond covariance?; References; 5 Stochastic simulation without random function theory; 5.1 Direct sampling; 5.1.1 Relying on information theory; 5.1.2 Application of direct sampling to Walker Lake.
- 5.2 The extended normal equation5.2.1 Formulation; 5.2.2 The RAM solution; 5.2.3 Single normal equations simulation for Walker Lake; 5.2.4 The problem of conditioning; 5.3 Simulation by texture synthesis; 5.3.1 Computer graphics; 5.3.2 Image quilting; References; 6 Returning to the Sierra Nevada; Reference; Part II Methods; 1 Introduction; 2 The algorithmic building blocks; 2.1 Grid and pointset representations; 2.2 Multivariate grids; 2.3 Neighborhoods; 2.4 Storage and restitution of data events; 2.4.1 Raw storage of training image; 2.4.2 Cross-correlation based convolution.
- 2.4.3 Partial convolution2.4.4 Tree storage; 2.4.5 List storage; 2.4.6 Clustering of patterns; 2.4.7 Parametric representation of patterns; 2.5 Computing distances; 2.5.1 Norms; 2.5.2 Hausdorff distance; 2.5.3 Invariant distances; 2.5.4 Change of variable; 2.5.5 Distances between distributions; 2.6 Sequential simulation; 2.6.1 Random path; 2.6.2 Unilateral path; 2.6.3 Patch-based methods; 2.6.4 Patch carving; 2.7 Multiple grids; 2.8 Conditioning; 2.8.1 The different types of data; 2.8.2 Different types of data: an example; 2.8.3 Steering proportions; References.