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Titlebook: Geostatistics for Engineers and Earth Scientists; Ricardo A. Olea Book 1999 Kluwer Academic Publishers 1999 Estimator.Kriging.Time series.

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書(shū)目名稱Geostatistics for Engineers and Earth Scientists
編輯Ricardo A. Olea
視頻videohttp://file.papertrans.cn/385/384108/384108.mp4
圖書(shū)封面Titlebook: Geostatistics for Engineers and Earth Scientists;  Ricardo A. Olea Book 1999 Kluwer Academic Publishers 1999 Estimator.Kriging.Time series.
描述Engineers and earth scientists are increasingly interested inquantitative methods for the analysis, interpretation, and modeling ofdata that imperfectly describe natural processes or attributesmeasured at geographical locations. Inference from imperfect knowledgeis the realm of classical statistics. In the case of many naturalphenomena, auto- and cross- correlation preclude the use of classicalstatistics. The appropriate choice in such circumstances isgeostatistics, a collection of numerical techniques for thecharacterization of spatial attributes similar to the treatment intime series analysis of auto-correlated temporal data. As in timeseries analysis, most geostatistical techniques employ randomvariables to model the uncertainty that goes with the assessments. Theapplicability of the methods is not limited by the physical nature ofthe attributes. ..Geostatistics for Engineers and Earth Scientists. presents aconcise introduction to geostatistics with an emphasis on detailedexplanations of methods that are parsimonious, nonredundant, andthrough the test of time have proved to work satisfactorily for avariety of attributes and sampling schemes. Most of these methods arevarious form
出版日期Book 1999
關(guān)鍵詞Estimator; Kriging; Time series; classification; correlation; data analysis; digital elevation model; simul
版次1
doihttps://doi.org/10.1007/978-1-4615-5001-3
isbn_softcover978-1-4613-7271-4
isbn_ebook978-1-4615-5001-3
copyrightKluwer Academic Publishers 1999
The information of publication is updating

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Ordinary Kriging, seen that simple kriging requires knowledge of the mean to solve the problem of finding weights that minimize the variance of the estimation error. Ordinary kriging elegantly discards the requirement by filtering out the mean, taking advantage of Corollary 2.11. And by removing the mean from the es
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Universal Kriging,d an effort to lift the constant mean requirement by moving from a global constant mean to a locally constant mean. There are several instances of attributes— water depth near the shore, temperature in the upper part of the earth’s crust, water table elevation in the High Plains aquifer of Kansas—th
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imperfectly describe natural processes or attributesmeasured at geographical locations. Inference from imperfect knowledgeis the realm of classical statistics. In the case of many naturalphenomena, auto- and cross- correlation preclude the use of classicalstatistics. The appropriate choice in such
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Sebastian Gawron,Sebastian Gerthributes— water depth near the shore, temperature in the upper part of the earth’s crust, water table elevation in the High Plains aquifer of Kansas—that have a clear systematic variation. Models that presume constancy of the mean are inadequate for the characterization of such attributes.
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