Iterative algorithms for fitting a linear model of coregionalization

In geostatistical applications, automated or semi-automated procedures are often used for modeling the spatial correlation structure (simple and cross variograms) of multivariate data. This paper deals with the well-known linear model of coregionalization and presents three iterative algorithms to f...

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Veröffentlicht in:Computers & geosciences Jg. 36; H. 9; S. 1150 - 1160
1. Verfasser: Emery, Xavier
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.09.2010
Elsevier
Schlagworte:
Algorithms
Cross covariance
Cross variogram
Deviation
Earth sciences
Earth, ocean, space
Exact sciences and technology
Fittings
Heterotopic sampling
Iterative algorithms
Mathematical analysis
Mathematical models
Matrices
Matrix methods
Metal geology
Metallic and non-metallic deposits
Multi-Gaussian model
Multivariate variogram analysis
Plurigaussian model
Proposals
Simulated annealing
South America
Chile
Multi-Gaussian model
Cross variogram
Multivariate variogram analysis
Simulated annealing
Plurigaussian model
Cross covariance
Heterotopic sampling
algorithms
iterative methods
computer programs
variograms
covariance
case studies
multivariate analysis
sills
copper
polymetallic ores
computers
geostatistics
mineral resources
sampling
data processing
least-squares
arsenic
linear models
correlation
silver
ISSN:0098-3004, 1873-7803
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Beschreibung
Zusammenfassung:In geostatistical applications, automated or semi-automated procedures are often used for modeling the spatial correlation structure (simple and cross variograms) of multivariate data. This paper deals with the well-known linear model of coregionalization and presents three iterative algorithms to find out coregionalization matrices that minimize a weighted sum of the squared deviations between sample and modeled variograms. The first one is a variation of Goulard and Voltz’s proposal for variogram fitting with no constraint other than mathematical consistency. The second one uses simulated annealing for fitting subject to constraints on the simple variogram sills. The third one is a nonlinear least squares algorithm for fitting a plurigaussian model. In all three algorithms, the sample variogram matrices need not be entirely known for every lag vector, a situation of interest with heterotopic samplings. To demonstrate the capabilities of the proposed algorithms, a set of computer programs is provided and applied to case studies in mineral resources evaluation.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2009.10.007