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Kategorie szczegółowe BISAC

Geostatistical Simulation: Models and Algorithms

ISBN-13: 9783540422020 / Angielski / Twarda / 2001 / 256 str.

Christian Lantuejoul; C. Lantuejoul
Geostatistical Simulation: Models and Algorithms Lantuejoul, Christian 9783540422020 Springer - książkaWidoczna okładka, to zdjęcie poglądowe, a rzeczywista szata graficzna może różnić się od prezentowanej.

Geostatistical Simulation: Models and Algorithms

ISBN-13: 9783540422020 / Angielski / Twarda / 2001 / 256 str.

Christian Lantuejoul; C. Lantuejoul
cena 403,47
(netto: 384,26 VAT:  5%)

Najniższa cena z 30 dni: 385,52
Termin realizacji zamówienia:
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Dostawa w 2026 r.

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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 construction of an estimator for the length starting from the depth estimator, but the depth estimator itself. Linear regression estimation requires only the mean and the covariance function. But the covariance function does not tell us much about the length of the trajectories. Figure 1.

Kategorie:
Nauka, Biologia i przyroda
Kategorie BISAC:
Science > Earth Sciences - Geology
Science > Geofizyka
Mathematics > Prawdopodobieństwo i statystyka
Wydawca:
Springer
Język:
Angielski
ISBN-13:
9783540422020
Rok wydania:
2001
Ilość stron:
256
Waga:
0.64 kg
Wymiary:
22.9 x 15.2
Oprawa:
Twarda
Wolumenów:
01
Dodatkowe informacje:
Bibliografia
Wydanie ilustrowane

From the reviews of the first edition:

"Geostatistical simulations have mainly been developed during the last decade. ... this is the first book that is entirely dedicated to this subject. ... it has been a good initiative by C. Lantuéjoul to compile this book and it will become a basic reference work, partly because it is the first work dedicated entirely to this new subject of geostatistics. ... The book mainly aims at researchers who are using geostatistical simulations and who would like to know more about the theoretical background ... ." (André Vervoort, Geologica Belgica, Vol. 7 (3-4), 2004)

"The author has dedicated the book to Georges Matheron, founder of modern geostatistics. Well organized is the book in three parts, namely (i) the tools, (ii) the algorithm and (iii) the models. ... It certainly fills a gap and is therefore welcome to the geostatistics market." (Erik W. Grafarend, Zentralblatt MATH, Vol. 990 (15), 2002)

1. Introduction.- 2. Investigating stochastic models.- 3. Variographic tools.- 4. The integral range.- 5. Basic morphological concepts.- 6. Stereology: some basic notions.- 7. Basics about simulations.- 8. Iterative algorithms for simulation.- 9. Rate of convergence of iterative algorithms.- 10. Exact simulations.- 11. Point processes.- 12. Tessellations.- 13. Boolean model.- 14. Object based models.- 15. Gaussian random function.- 16. Gaussian variations.- 17. Substitution random functions.

Within the geoscience community the estimation of natural resources is a challenging topic. The difficulties are threefold: Intitially, the design of appropriate models to take account of the complexity of the variables of interest and their interactions. This book discusses a wide range of spatial models, including random sets and functions, point processes and object populations. Secondly,the construction of algorithms which reproduce the variability inherent in the models. Finally, the conditioning of the simulations for the data, which can considerably reduce their variability. Besides the classical algorithm for gaussian random functions, specific algorithms based on markovian iterations are presented for conditioning a wide range of spatial models (boolean model, Voronoi tesselation, substitution random function etc.) This volume is the result of a series of courses given in the USA and Latin America to civil, mining and petroleum engineers, as well as to gradute students is statistics. It is the first book to discuss geostatistical simulation techniques in such a systematic way.



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