Geostatistics for engineers and earth scientists工程师和地球科学家用大地统计学

出版时间：1999-5  作者：Olea, Ricardo A.  页数：303  

内容概要

　　Engineers and earth scientists are increasingly interested in quantitative methods for the analysis, interpretation, and modeling of data that imperfectly describe natural processes or attributes measured at geographical locations. Inference from imperfect knowledge is the realm of classical statistics. In the case of many natural phenomena, auto- and cross- correlation preclude the use of classical statistics. The appropriate choice in such circumstances is geostatistics, a collection of numerical techniques for the characterization of spatial attributes similar to the treatment in time series analysis of auto-correlated temporal data. As in time series analysis, most geostatistical techniques employ random variables to model the uncertainty that goes with the assessments. The applicability of the methods is not limited by the physical nature of the attributes. 　　Geostatistics for Engineers and Earth Scientists presents a concise introduction to geostatistics with an emphasis on detailed explanations of methods that are parsimonious, nonredundant, and through the test of time have proved to work satisfactorily for a variety of attributes and sampling schemes. Most of these methods are various forms of kriging and stochastic simulation. The presentation follows a modular approach making each chapter as self-contained as possible, thereby allowing for reading of individual chapters, reducing excessive cross-referencing to previous results and offering possibilities for reviewing similar derivations under slightly different circumstances. Guidelines and rules are offered wherever possible to help choose from among alternative methods and to select parameters, thus relieving the user from making subjective calls based on an experience that has yet to be acquired. 　　Geostatistics for Engineers and Earth Scientists is intended to assist in the formal teaching of geostatistics or as a self tutorial for anybody who is motivated to employ geostatistics for sampling design, data analysis, or natural resource characterization. Real data sets are used to illustrate the application of the methodology.

书籍目录

List of Mathematical DefinitionsUst of TheoremsList of CorollariesList of LemmasPrefaceChapter 1: IntroductionChapter 2: Simple Kriging　Properties of Linear Combinations of Variates　Assumptions and Definitions　The Estimation Variance　Normal Equations　Minimum Mean Square Error　Algorithm　　EXERCISE 2. 1　Properties　　EXERCISE 2.2Chapter 3: Normalization　Comparing Two Distributions　  EXERCISE 3.1　Normal Score Transformation　Simple Kriging of Normal Scores　　EXERCISE 3.2Chapter 4: Ordinary Kriging　Assumptions　Important Relationships　The Estimator　The Estimation Variance　The Optimization Problem　Minimum Mean Square Error　Algorithm for Intrinsic Random Functions  Second Order Stationary Ordinary Kriging　  EXERCISE 4.1  Properties  Relating Simple and Ordinary Kriging  Search Neighborhood  Quasi-Stationary EstimatorChapter 5: The Semivariogram  The Semivariogram of the Random Function  The Experimental Semivariogram　  EXERCISE 5. 1  Anisotropy and Drift  Semivariogram Models  Additivity  Parameter Estimation by Trial and Error  Automatic Parameter Fitting　  EXERCISE 5.2  Support  Direct ApplicationsChapter 6: Universal Kriging  The Estimator  Assumptions  Unbiasedness  Estimation Variance  Optimization  Minimum Mean Square Error  Algorithm for Intrinsically Stationary Residuals   Second Order Stationary Universal Kriging  Practice　  EXERCISE 6.1Chapter 7: Crossvalidation  Alternative Evaluation Method　  EXERCISE 7.1  Diagnostic Statistics　  EXERCISE 7.2Chapter 8: Drift and Residuals  Assumptions  Unbiasedness  Estimation Variance  Optimal Estimator  Minimum Estimation Variance  Algorithmic Summary  Residuals　  EXERCISE 8.1　……Chapter 9: Stochastic SimulationChapter 10: RellabilityChapter 11: Cumulative Distribution EstimatorsChapter 12: Block KrigingChapter 13: Ordinary CokrigingChapter 14: Regionalized ClassificationReferencesAppendix A:West Lyons Field SamplingAppendix B:High Plains Aquifer SamplingAppendix C:UNCF SamplingAppendix D:Dakota Aquifer SamplingAuthor IndexSubject Index