Titlebook: Geostatistics and Petroleum Geology; Michael Edward Hohn Book 1988 Springer Science+Business Media New York 1988 Kriging.geology.geoscienc

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https://doi.org/10.1007/978-3-658-44080-0s the name implies, a semivariogram is a measure of variance. Although procedures exist for modeling the semivariogram through iterative or least-squares methods, practitioners recommend actual inspection of the observed semivariogram and the fitted model. A properly fitted model then allows the com

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Howard H. Stevenson,J. Carlos Jarillos, to drawing flat surfaces within triangulations, all requiring some criterion to be satisfied. The geologist working by hand interpolates between pairs of points, draws connecting contours, does some smoothing to make the map look “real,” and perhaps works in some trends derived from geological ex

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The entrepreneurial financial model,he expected precision of the estimates. However, some applications may call for knowledge of local spatial distributions. Placing confidence intervals around local estimates; stating the probability that a regionalized variable lies below, between, or above some values; computing mean values of a va

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Kreativität, Innovation und Entrepreneurshipre more easily treated if converted to a nominal scale. As a simple example of such an ., the presence or absence of a show of gas may be represented by the two values 0 and 1. Statistics like success ratio assume a value for economic threshold that may not be stated. A more explicit use of a thresh

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https://doi.org/10.1007/978-3-662-57984-8There are several situations where one may want to study and exploit the covariance between two or more regionalized variables.

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Multivariate Geostatistics,There are several situations where one may want to study and exploit the covariance between two or more regionalized variables.

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https://doi.org/10.1007/978-3-658-26800-8s a map that honors observed values of the regionalized variable, has the same degree of fine-scale variability as the observed data, and obeys the same spatial law. By relaxing the requirement of minimal squared error, conditional simulation sacrifices some certainty for more detail.

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More Detail, Less Certainty: Conditional Simulation,s a map that honors observed values of the regionalized variable, has the same degree of fine-scale variability as the observed data, and obeys the same spatial law. By relaxing the requirement of minimal squared error, conditional simulation sacrifices some certainty for more detail.