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Geostatistical Error Management. Quantifying Uncertainty for Environmental Sampling and Mapping. Edition No. 1

  • ID: 2220177
  • Book
  • April 1997
  • 571 Pages
  • John Wiley and Sons Ltd
Geostatistical Error Management Geostatistical modeling conceptsand techniques have become daily practice in mining operations.That's because these precise analytical tools help professionalsquantify uncertainty and make objective decisions in the face ofthorny "real world" challenges. Geostatistical Error Management isthe first book to apply these proven quantitative tools toenvironmental challenges. The centerpiece of this working guide isan innovative decision-making framework, known as geostatisticalerror management (GEM). GEM integrates the related areas of DataQuality Objectives, Sampling Theory & Practice, andGeostatistical Appraisal to create an entirely new set of toolsthat help you more accurately assess resources for collectingenvironmental data, analyze sources of error in sampling, andquantify the extent and levels of contamination at environmentallyimpacted sites needing remediation. This practical,results-oriented resource

Focuses on the environmental applications of geostatisticaltechniques and how they fit into today's regulatory, legal, andengineering environments

Provides step-by-step explanations for applying error managementtools at every stage of an environmental site assessment

Points the way to applying GEM to environmental work beyond siteevaluation and characterization
Geostatistical Error Management will enable environmentalspecialists to perform assessments of hazardous waste andenvironmentally impacted sites more accurately and to confidentlymanage uncertainty and error at every phase of a remediationproject.
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INTRODUCTION TO GEOSTATISTICAL ERROR MANAGEMENT.

Foundations of Geostatistical Error Management.

GEM Perspectives.

Introduction to Error.

STATISTICAL CONSIDERATIONS.

Foundations of Statistics.

Data Distributions.

Distributional Models.

SAMPLING THEORY AND PRACTICE.

Heterogeneity and Sampling.

Sampling Errors.

GEOSTATISTICAL APPRAISAL.

Bivariate Distributions.

Variograms: Quantification of Spatial Continuity.

The Volume-Variance Relationship.

Estimation Variance.

Optimizing Estimation: Kriging.

Practical Aspects of Kriging.

DATA QUALITY OBJECTIVES.

Data Quality Objectives.

Integrating DQOs and STP: Development of Sampling Strategies.

Integrating DQOs and GA: Mapping and Appraisal.

Appendices.

References.

Index.
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Jeffrey C. Myers
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