A New Method for Handling the Nugget Effect in Kriging
P. Krivoruchko, A. Gribov, J. M. Ver Hoef, 2006. "A New Method for Handling the Nugget Effect in Kriging", Stochastic Modeling and Geostatistics: Principles, Methods, and Case Studies, Volume II, T. C. Coburn, J. M. Yarus, R. L. Chambers
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This chapter discusses methods for estimating the nugget in semivariogram models. Commonly used exact and filtered kriging methods are compared with an alternative method, which predicts a new value at the sampled location. Using the alternative method, estimation at a location where data have been collected involves predicting the smooth underlying value plus a new observation from measurement error. This is exactly what is necessary for validation and crossvalidation diagnostics. Three examples of using new value kriging are presented that involve comparison of simulated results, porosity estimation for the North Cowden unit in west Texas, and analysis of radiocesium soil-contamination data collected in Belarus after the Chernobyl accident.
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Since publication of the first volume of Stochastic Modeling and Geostatistics in 1994, there has been an explosion of interest and activity in geostatistical methods and spatial stochastic modeling techniques. Many of the computational algorithms and methodological approaches that were available then have greatly matured, and new, even better ones have come to the forefront. Advances in computing and increased focus on software commercialization have resulted in improved access to, and usability of, the available tools and techniques. Against this backdrop, Stochastic Modeling and Geostatistics Volume II provides a much-needed update on this important technology. As in the case of the first volume, it largely focuses on applications and case studies from the petroleum and related fields, but it also contains an appropriate mix of the theory and methods developed throughout the past decade. Geologists, petroleum engineers, and other individuals working in the earth and environmental sciences will find Stochastic Modeling and Geostatistics Volume II to be an important addition to their technical information resources.