Geostatistical Error Management. Quantifying Uncertainty for Environmental Sampling and Mapping

  • ID: 2220177
  • Book
  • 572 Pages
  • John Wiley and Sons Ltd
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Geostatistical Error Management Geostatistical modeling concepts and techniques have become daily practice in mining operations. That’s because these precise analytical tools help professionals quantify uncertainty and make objective decisions in the face of thorny "real world" challenges. Geostatistical Error Management is the first book to apply these proven quantitative tools to environmental challenges. The centerpiece of this working guide is an innovative decision–making framework, known as geostatistical error management (GEM). GEM integrates the related areas of Data Quality Objectives, Sampling Theory & Practice, and Geostatistical Appraisal to create an entirely new set of tools that help you more accurately assess resources for collecting environmental data, analyze sources of error in sampling, and quantify the extent and levels of contamination at environmentally impacted sites needing remediation. This practical, results–oriented resource
  • Focuses on the environmental applications of geostatistical techniques and how they fit into today’s regulatory, legal, and engineering environments
  • Provides step–by–step explanations for applying error management tools at every stage of an environmental site assessment
  • Points the way to applying GEM to environmental work beyond site evaluation and characterization
Geostatistical Error Management will enable environmental specialists to perform assessments of hazardous waste and environmentally impacted sites more accurately and to confidently manage uncertainty and error at every phase of a remediation project.
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Foundations of Geostatistical Error Management.

GEM Perspectives.

Introduction to Error.


Foundations of Statistics.

Data Distributions.

Distributional Models.


Heterogeneity and Sampling.

Sampling Errors.


Bivariate Distributions.

Variograms: Quantification of Spatial Continuity.

The Volume–Variance Relationship.

Estimation Variance.

Optimizing Estimation: Kriging.

Practical Aspects of Kriging.


Data Quality Objectives.

Integrating DQOs and STP: Development of Sampling Strategies.

Integrating DQOs and GA: Mapping and Appraisal.



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About the Author Jeffrey C. Myers has over twenty years experience applying geostatistical methods to practical problems in both the mining and environmental industries. His work has included ore reserve estimation on projects involving commodities such as gold, silver, platinum, copper, lead, zinc, uranium, coal, lignite, gypsum, limestone, trona, diatomite, and sand and gravel. He participated in the development of commercial geostatistical software and installed customized ore grade control stations at working mines. Mr. Myers has extensive experience modeling hazardous chemicals in soils and groundwater, including VOCs, pesticides, heavy metals, PCBs, PAHs, and radionuclides, and has supplied expert support on numerous Superfund litigations. He has taught public and private geostatistical short courses for over a decade and taught graduate–level courses in environmental statistics. Mr. Myers holds a Bachelor of Science degree in Geology from West Virginia University and a Masters degree in Mining Engineering from the Colorado School of Mines.
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Note: Product cover images may vary from those shown