Basic Steps In Geostatistics The Variogram And Kriging
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Basic Steps in Geostatistics: The Variogram and Kriging
This brief will provide a bridge in succinct form between the geostatistics textbooks and the computer manuals for `push-button' practice. It is becoming increasingly important for practitioners, especially neophytes, to understand what underlies modern geostatistics and the currently available software so that they can choose sensibly and draw correct conclusions from their analysis and mapping. The brief will contain some theory, but only that needed for practitioners to understand the essential steps in analyses. It will guide readers sequentially through the stages of properly designed sampling, exploratory data analysis, variography (computing the variogram and modelling it), followed by ordinary kriging and finally mapping kriged estimates and their errors. There will be short section on trend and universal kriging. Other types of kriging will be mentioned so that readers can delve further in the substantive literature to tackle more complex tasks.
Spatial and Spatio-Temporal Geostatistical Modeling and Kriging
Author: José-María Montero
language: en
Publisher: John Wiley & Sons
Release Date: 2015-08-18
Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R. This book includes: Methods for selecting valid covariance functions from the empirical counterparts that overcome the existing limitations of the traditional methods. The most innovative developments in the different steps of the kriging process. An up-to-date account of strategies for dealing with data evolving in space and time. An accompanying website featuring R code and examples
Kriging in Slope Reliability Analysis
Kriging can be used to determine optimal unbiased predictions for regionalized variables and has been shown to be a powerful tool in slope reliability analysis for reliability-based design. This is the first book to systematically cover the basic theory and applications of the method in slope reliability assessment. The book gives an extensive and detailed presentation of principles and applications, introducing geostatistics and the basic theory of Kriging before addressing the challenges in the application of Kriging in slope reliability analysis. The latest advancements in Kriging application methods are introduced, which enhance computational accuracy and reduce model errors. These include optimization algorithms for spatial parameters in Kriging, adaptive modeling of spatial correlation structures, efficient sampling methods based on Monte Carlo simulation, quantitative analysis of slope failure risks, and reliability analysis methods for unreinforced and reinforced slopes based on conditional random fields. Several case studies are presented to illustrate the practical application and implementation procedures, bridging theory, and practical engineering. Kriging in Slope Reliability Analysis particularly suits consulting engineers, researchers, and postgraduate students.