Geostatistics for Engineers and Earth Scientists

Olea, Ricardo A.

doi:10.1007/978-1-4615-5001-3

Cited by 418 publications

(371 citation statements)

References 0 publications

Supporting

Mentioning

311

Contrasting

Unclassified

Order By: Relevance

“…Because the nugget variances in the cross-semivariograms did not disappear or significantly reduce in most instances, this nugget value perhaps better describes variability occurring within the shortest sampling interval rather than laboratory analytical errors (Goovaerts 1999;Olea 1999). Examples of similar patterns of behaviour for cross-semivariogram nugget variances at different spatial scales in mapping soil properties have been published by Paz González et al (2000) in small agricultural and forest plots, sized 100 m 2 , and by Webster et al (1994) For personal use only.…”

Section: Semivariogram and Cross-semivariogram Analysismentioning

confidence: 84%

“…Common practice has shown that cross-semivariograms present smaller nugget variance than individual direct semivariograms (Goovaerts 1999;Olea 1999). In order to investigate if coregionalisation could improve the description of spatial continuity and reduce the estimation errors of the kriging variance, cross-semivariograms for all heavy metal pairs that had positive significant correlations were constructed.…”

Section: Semivariogram and Cross-semivariogram Analysismentioning

confidence: 99%

“…2b. Intrinsic coregionalisation is referred to as the case when all semivariograms and cross-semivariograms are proportional to each other (Olea 1999). Because of the different ranges of spatial dependence of the structures presented in Figs.…”

Section: Semivariogram and Cross-semivariogram Analysismentioning

confidence: 99%

See 2 more Smart Citations

Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area

Paz‐González¹,

Taboada-Castro²,

Vieira³

2001

Can. J. Soil. Sci.

View full text Add to dashboard Cite

S. R. 2001. Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area. Can. J. Soil Sci. 81: 469-479. The objective of this study was to examine the spatial variability of selected heavy metals in a soil developed over serpentine. Both total and EDTA-extractable Fe, Mn, Cr, Ni, Cu, Zn and Co were determined in 53 samples, collected from the topsoil of a 1-ha forested plot. Naturally occurring soil Cr and Ni concentrations were much higher than critical limits for safety. Experimental semivariograms were computed and modelled by a nugget component plus a structure with autocorrelation ranges varying between 25 and 90 m. EDTA-extractable heavy metal contents exhibit a different spatial variation pattern from that of total contents, although Ni and Cu semivariograms present some similarities. The joint spatial variation for pairs of variables with significant correlation was also investigated. The nugget variances in the cross-semivariograms were not very different from those of individual semivariograms, suggesting heterogeneity within the shortest sampling interval. Semivariograms provided a clear description of the spatial structure of heavy metals and some insight into possible processes affecting their distribution. Kriging maps allowed the identification of small regions with distinct metal concentrations and confirmed the suitability of geostatistics for investigating processes controlling heavy metal variation. Isotopic cokriging performed better than kriging, but the gain for mapping purposes was limited.

show abstract

Section: Semivariogram and Cross-semivariogram Analysismentioning

confidence: 84%

Section: Semivariogram and Cross-semivariogram Analysismentioning

confidence: 99%

See 1 more Smart Citation

Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area

Paz‐González¹,

Taboada-Castro²,

Vieira³

2001

Can. J. Soil. Sci.

View full text Add to dashboard Cite

show abstract

“…The most commonly used models are Gaussian, spherical, power, exponential, and cubic (Olea 1999). There are, however, no statistical tests to evaluate an experimental variogram and to determine how closely the theoretical variogram is approximated.…”

Section: Kriging Procedures 241 Ordinary Krigingmentioning

confidence: 99%

“…There are, however, no statistical tests to evaluate an experimental variogram and to determine how closely the theoretical variogram is approximated. In this paper, to do automatic fitting we use the least square method and the Akaike information criterion (AIC) over the sum of the squares between the sample variogram and the fitted variogram model (Olea 1999). For our data, the Stein-Matern parameterization developed by Stein (1999) yields the best fit:…”

Section: Kriging Procedures 241 Ordinary Krigingmentioning

confidence: 99%

Modelling spatio-temporal variability of temperature

et al. 2015

View full text Add to dashboard Cite

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractForecasting temperature in time and space is an important precondition for both the design of weather derivatives and the assessment of the hedging effectiveness of index based weather insurance. In this article, we show how this task can be accomplished by means of Kriging techniques. Moreover, we compare Kriging with a dynamic semiparametric factor model (DSFM) that has been recently developed for the analysis of high dimensional financial data. We apply both methods to comprehensive temperature data covering a large area of China and assess their performance in terms of predicting a temperature index at an unobserved location. The results show that the DSFM performs worse than standard Kriging techniques. Moreover, we show how geographic basis risk inherent to weather derivatives can be mitigated by regional diversification.

show abstract

Spatial Bayesian inversion with localized likelihoods: An exact sampling alternative to MCMC

Walker

Curtis

2014

JGR Solid Earth

View full text Add to dashboard Cite

Geoscientists often use spatially discretized cellular models of the Earth where data in each grid cell provide independent information about the model parameters of interest at that location. In Bayesian inference this information is given as a set of likelihoods describing the (unnormalized) probability of the parameters, given only the data in each cell. Preexisting information about the model parameters' values and their spatial correlations may be described by a prior probability distribution. The prior, likelihoods, and Bayes' rule together specify a posterior probability distribution that describes the resultant state of information over all model parameters. However, due to the high dimensionality of typical models, the posterior is usually only known up to a multiplicative constant and only at specific, numerically evaluated points in the model space (i.e., it is not known analytically). Markov chain Monte Carlo (MCMC) methods are typically used to produce an ensemble of correlated samples from the posterior. These ensembles are slow to converge in distribution to the posterior; indeed, they may not converge in finite time, and detecting their state of convergence is often impossible in practice. Thus, estimates of the posterior obtained in this way may be biased. We derive a recursive algorithm which samples the posterior exactly, so as to avoid these convergence issues. Its computational cost scales with the size of the parameters' sample space, the prior's spatial range of dependency, and the shortest edge dimension of the grid. We develop an approximation to the algorithm such that it may be used on large 2‐D (and potentially 3‐D) model grids. We apply it to synthetic seismic attribute data and obtain results which compare favorably to the results of MCMC (Gibbs) sampling—which exhibits convergence problems.

show abstract

Geostatistics for Engineers and Earth Scientists

Cited by 418 publications

References 0 publications

Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area

Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area

Modelling spatio-temporal variability of temperature

Spatial Bayesian inversion with localized likelihoods: An exact sampling alternative to MCMC

Contact Info

Product

Resources

About