Geostatistical Error Management: Quantifying Uncertainty for Environmental Sampling and Mapping
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More About This Title Geostatistical Error Management: Quantifying Uncertainty for Environmental Sampling and Mapping
English
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.
* 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.
English
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.
English
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.
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.
