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

1999-5  

Olea, Ricardo A.  

303  

　　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.

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

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