The Factorial Kriging Analysis of Regionalized Data. Its Application to Geochemical Prospecting

Sandjivy, L.

doi:10.1007/978-94-009-3699-7_32

Cited by 43 publications

(9 citation statements)

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“…This is effectively a form of spatial filtering in which factors are distinguished according to spatial scale at which they contribute to the variance. The technique ha5 already been used in geophysics (Chile's & Guillen, 1984;Galli et al, 1984), in geochemistry (Sandjivy, 1983), in geology (Jaquet, 1989), and most recently in soil science (Goovaerts, 1 9 9 2~) and in hydrogeology (Goovaerts et ul., 1993).…”

Section: Introductionmentioning

confidence: 99%

Scale‐dependent correlation between topsoil copper and cobalt concentrations in Scotland

Goovaerts

Webster

1994

European J Soil Science

109

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The soil of south-east Scotland is locally deficient in copper and cobalt. Mcasurcments from nearly 3000 fields for which the soil association is known were analysed to study the coregionalization of the two elements and to assess the influence of parent material on the metals' concentrations. The experimental auto-and cross-variograms revealed distinct local ( I .5 km) and regional (20 km) scales of spatial variation. A combination of indicator variograms of the soil associations had the same spatial structures, suggesting that parent material influences the concentrations of the metals.The coregionalization between copper and cobalt was modelled as a linear combination of three spatial structures. The resulting structural correlation coefficients showed the two elements to be fairly strongly positively correlated at the regional scale. Kriging allowed determination and mapping of each spatial component; these maps were then compared with the spatial distribution of soil associations in the region. An analysis of variance was performed before and after filtering out the nugget and short-range spatial components. Classification by soil association (parent material) accounted for a large proportion of the variance at the regional scale, suggesting that the parent material contributes substantially more to the trace element content of the soil than had been thought earlier.

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Section: Introductionmentioning

confidence: 99%

Scale‐dependent correlation between topsoil copper and cobalt concentrations in Scotland

Goovaerts

Webster

1994

European J Soil Science

109

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“…Sample residual data belong to the CC and are the part non-accounted for in the previous attributes, and therefore can be computed by sequential cokriging (Vargas and Yeh, 1999). Computing residual data is analogous to filtering (Sandjivy, 1984). If the data is non-Gaussian and cokriging is going to be used, back and forth data normalization may be required except if the collocated cokriging approach is suitable as will be discussed in the next point.…”

Section: Joint Conditional Simulation By Generation Of CC Random Fieldsmentioning

confidence: 99%

Conditional components for simulation of vector random fields

Vargas-Guzmán

2003

Stochastic Environmental Research and Risk Assessment (SERRA)

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Conditional component random fields (CC) based on Cholesky decomposition of the multivariate spectra are introduced in this study to develop a new method for conditional simulation of vector attributes in environmental and geological phenomena. The CC are independent random fields with covariance models obtained from projections and conditioning in the frequency domain. The approach is to simulate one attribute in the physical space and use the results to estimate the other attributes in the frequency domain. Then, a CC for the next attribute is simulated and projected on the other attributes. In general, any attribute is built as the sum of inverse Fourier transform of the orthogonal projection of previous simulated CC plus a last CC simulated in the physical space. This simulation approach continues in this fashion for several attributes and the order of them may be changed for different realizations. This method allows for data conditioning and simulation. A simplified version for intrinsically correlated random fields allows for an approach that avoids the frequency domain.

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“…Factorial kriging analysis (Sandjivy 1984;Jaquet 1989;Goovaerts 1992;Goovaerts 1997; Wackernagel 2003) is a geostatistical approach that combines principal component analysis (PCA) together with spatial modelling and prediction. It allows the study and interpretation of multivariate data, taking into account the spatial correlation structure of these data and distinguishing different scales of spatial variation, via the modelling of the direct and cross variograms of the variables under study, the latter also known as 'co-regionalized variables'.…”

Section: Factorial Kriging Analysismentioning

confidence: 99%

Soil gas geochemical exploration in covered terrains of northern Chile: data processing techniques and interpretation of contrast anomalies

Castillo

Townley

Emery

et al. 2015

GEEA

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The Campanani, Casualidad and Inca de Oro prospects, owned by Codelco-Chile, are extensive areas covered by post-mineralization deposits. To assist the detection of potential exploration targets, we carried out basic soil gas sampling campaigns by means of the Ore Hound GOCC® passive collectors, which were buried on a regular grid at a depth of 30-40 cm. After 90-120 days, samples were retrieved, sealed, and adsorbent material was submitted to an acid elution and analysed by ICP-MS for a set of 72 elements. In this study, databases have been analysed and modelled by statistical and geostatistical methodologies, mainly by factorial kriging analysis (FK). In all cases, a systematic analytical error was detected, discriminated and filtered, allowing the definition of contrast anomalies at different scales of spatial variation, and the interpretation of evidence of secondary dispersion processes by which present time surface contrast anomalies have been developed.The interpretation of contrast anomalies and element associations allowed the recognition of structural influences as pathways for gaseous circulation at the Campanani and Casualidad prospects. Likewise, lithological controls were detected at the Campanani and Inca de Oro prospects. At the Casualidad Prospect, a Co-Sn-Cu association describes a prospective zone c. 2 km north of the Casualidad deposit. At the Inca de Oro prospect, a marked influence of groundwater flow and/or surface drainage has been recognized in the configuration of contrast anomalies.

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The Factorial Kriging Analysis of Regionalized Data. Its Application to Geochemical Prospecting

Cited by 43 publications

References 0 publications

Scale‐dependent correlation between topsoil copper and cobalt concentrations in Scotland

Scale‐dependent correlation between topsoil copper and cobalt concentrations in Scotland

Conditional components for simulation of vector random fields

Soil gas geochemical exploration in covered terrains of northern Chile: data processing techniques and interpretation of contrast anomalies

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