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Titlebook: Geostatistical Simulation; Models and Algorithm Christian Lantuéjoul Textbook 2002 Springer-Verlag Berlin Heidelberg 2002 Conditional Simul

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書目名稱Geostatistical Simulation
副標(biāo)題Models and Algorithm
編輯Christian Lantuéjoul
視頻videohttp://file.papertrans.cn/385/384093/384093.mp4
概述Clear separation of models, from methods and algorithms.Coherent overview of the subject.Includes supplementary material:
圖書封面Titlebook: Geostatistical Simulation; Models and Algorithm Christian Lantuéjoul Textbook 2002 Springer-Verlag Berlin Heidelberg 2002 Conditional Simul
描述1. 1 Simulation versus estimation The following problem was raised by Alfaro (1979). A submarine cable has to be laid across the straits of Gibraltar. How can its length be predicted if the depth of the sea floor has been measured sparsely along its trajectory? Fig. 1. 1. Part of the actual trajectory and sample data points An exact determination of the length requires knowledge of the depth at each point of the trajectory. But these are mostly unknown. In a geostatistical set- ting, they are considered as random and can be estimated by linear regression starting from the available data points. This suggests estimating the actual length as the length of the estimated trajectory. The results turn out to be disappointing. The length of the trajectory is seriously underestimated (see Figure 1. 2). Clearly, the estimated trajectory is much smoother than the actual one. Fig. 1. 2. Part of the actual trajectory and its estimate from linear regression. In this particular example, the estimated trajectory is piecewise linear because the linear regression has been carried out using an exponential covariance function 2 1. Introduction What is really questionable in this procedure is not the
出版日期Textbook 2002
關(guān)鍵詞Conditional Simulations; Stereology; Stochastic Geometry; digital elevation model; mining; statistics; min
版次1
doihttps://doi.org/10.1007/978-3-662-04808-5
isbn_softcover978-3-642-07582-7
isbn_ebook978-3-662-04808-5
copyrightSpringer-Verlag Berlin Heidelberg 2002
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Point processesaracter of their constitutive elements, point processes can exhibit rather complicated structures (cf. Figure 11.1) . Secondly, they can be used for modelling a wide range of natural phenomena (trees in a forest, precious stone deposits, earthquake epicentres, stars in galaxies, populations in settl
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Object based modelsnt objects are combined. In this chapter, three combination rules are successively considered, namely the addition, the maximum and the superposition, which give rise respectively to the ., the . and the ..
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Gaussian random functionn their first two moments. These are .. Their main statistical properties are reviewed, the texture of their realizations is examined, and algorithms are proposed to simulate them, conditionnally or not.
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