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Geostatistics with location -depende...

Machuca-Mory, David Francisco.

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Geostatistics with location -dependent statistics.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Geostatistics with location -dependent statistics./
Author:	Machuca-Mory, David Francisco.
Description:	199 p.
Notes:	Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7686.
Contained By:	Dissertation Abstracts International71-12B.
Subject:	Engineering, Mining. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR62882
ISBN:	9780494628829

Geostatistics with location -dependent statistics.
Machuca-Mory, David Francisco.

Geostatistics with location -dependent statistics. - 199 p.

Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7686.

Thesis (Ph.D.)--University of Alberta (Canada), 2010.

In Geostatistical modelling of the spatial distribution of rock attributes, the multivariate distribution of a Random Function defines the range of possible values and the spatial relationships among them. Under a decision of stationarity, the Random Function distribution and its statistics are inferred from data within a spatial domain deemed statistically homogenous. Assuming stationary multiGaussianity allows spatial prediction techniques to take advantage of this simple parametric distribution model. These techniques compute the local distributions with surrounding data and global spatially invariant statistics. They often fail to reproduce local changes in the mean, variability and, particularly, the spatial continuity, that are required for geologically realistic modelling of rock attributes. The proposed alternative is to build local Random Function models that are deemed stationary only in relation to the locations where they are defined. The corresponding location-dependent distributions and statistics are inferred by weighting the samples inversely proportional to their distance to anchor locations. These distributions are locally Gaussian transformed. The transformation models carry information on the local histogram. The distance weighted experimental measures of spatial correlation are able to adapt to local changes in the spatial continuity and are semi-automatically fitted by locally defined variogram models. The fields of local variogram and transformation parameters are used in locally stationary spatial prediction algorithms. The resulting attribute models are rich in non-stationary spatial features. This process implies a higher computational demand than the traditional techniques, but, if data is abundant enough to allow a reliable inference of the local statistics, the proposed locally stationary techniques outperform their stationary counterparts in terms of accuracy and precision. These improved models have the potential of providing better decision support for engineering design.

ISBN: 9780494628829Subjects--Topical Terms:

1035560
Engineering, Mining.

Geostatistics with location -dependent statistics.
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020 $a 9780494628829
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100 1 $a Machuca-Mory, David Francisco. $3 2102113
245 1 0 $a Geostatistics with location -dependent statistics.
300 $a 199 p.
500 $a Source: Dissertation Abstracts International, Volume: 71-12, Section: B, page: 7686.
502 $a Thesis (Ph.D.)--University of Alberta (Canada), 2010.
520 $a In Geostatistical modelling of the spatial distribution of rock attributes, the multivariate distribution of a Random Function defines the range of possible values and the spatial relationships among them. Under a decision of stationarity, the Random Function distribution and its statistics are inferred from data within a spatial domain deemed statistically homogenous. Assuming stationary multiGaussianity allows spatial prediction techniques to take advantage of this simple parametric distribution model. These techniques compute the local distributions with surrounding data and global spatially invariant statistics. They often fail to reproduce local changes in the mean, variability and, particularly, the spatial continuity, that are required for geologically realistic modelling of rock attributes. The proposed alternative is to build local Random Function models that are deemed stationary only in relation to the locations where they are defined. The corresponding location-dependent distributions and statistics are inferred by weighting the samples inversely proportional to their distance to anchor locations. These distributions are locally Gaussian transformed. The transformation models carry information on the local histogram. The distance weighted experimental measures of spatial correlation are able to adapt to local changes in the spatial continuity and are semi-automatically fitted by locally defined variogram models. The fields of local variogram and transformation parameters are used in locally stationary spatial prediction algorithms. The resulting attribute models are rich in non-stationary spatial features. This process implies a higher computational demand than the traditional techniques, but, if data is abundant enough to allow a reliable inference of the local statistics, the proposed locally stationary techniques outperform their stationary counterparts in terms of accuracy and precision. These improved models have the potential of providing better decision support for engineering design.
590 $a School code: 0351.
650 4 $a Engineering, Mining. $3 1035560
650 4 $a Engineering, Geological. $3 1035566
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690 $a 0466
710 2 $a University of Alberta (Canada). $3 626651
773 0 $t Dissertation Abstracts International $g 71-12B.
790 $a 0351
791 $a Ph.D.
792 $a 2010
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR62882