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TitreSpatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario
AuteurGrunsky, E C; Agterberg, F P
SourceJournal of the International Association For Mathematical Geology vol. 20, no. 7, 1988 p. 825-861, https://doi.org/10.1007/bf00890195
Année1988
Séries alt.Commission géologique du Canada, Contributions aux publications extérieures 21387
ÉditeurSpringer Nature
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.1007/bf00890195
Mediapapier; en ligne; numérique
Formatspdf
ProvinceOntario
SNRC32D/08
Lat/Long OENS -80.0000 -79.5000 48.5000 48.2500
Sujetslithogéochimie; roches ignées; roches volcaniques; roches métamorphiques; analyse factorielle; géologie du substratum rocheux; analyses géochimiques; Subprovince d'Abitibi ; géomathématique; géochimie; Précambrien; Protérozoïque
Illustrationstables; sketches
Résumé(disponible en anglais seulement)
A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO 2 are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO 2 ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.
GEOSCAN ID126464