Application of geostatistics to identify gold-rich areas in the Finisterre-Fervenza region,NW Spain |
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| Institution: | 1. Departamento de Geolog??a, Escuela Politécnica Superior, Universidad de Jaén, Virgen de la Cabeza, 2, 23071 Jaén, Spain;2. Departamento de Geodinámica, Facultad de Ciencias, Universidad de Granada/IACT, Fuentenueva s/n, 18071 Granada Spain;1. Ores and Mineralization Group, University of Leeds, School of Earth and Environment, Leeds, LS2 9JT, United Kingdom;2. ToroGold Limited, Piccadilly, St. James''s, London, W1J 9EJ, United Kingdom;1. Norwegian Radiation Protection Authority, P.O. Box 55, 1332 Østerås, Norway;2. Centre for Environmental Radioactivity (CERAD CoE), P.O. Box 5003, NO-1432 Ås, Norway;3. Buskerud Telemark Vestfold County Councils, Fylkeshuset, P.O. 2163, NO-3103 Tønsberg, Norway;1. Department of Mining Engineering, University of Chile, Santiago, Chile;2. Advanced Mining Technology Center, University of Chile, Santiago, Chile;1. Instituto Español de Oceanografía, C.O.A Coruña. Paseo Marítimo Alcalde Francisco Vázquez, 10, 15001 A Coruña, Spain;2. Instituto Tecnolóxico para o Control do Medio Mariño de Galicia, Peirao de Vilaxoán s/n, 36611 Vilagarcía de Arousa, Pontevedra, Spain;3. Instituto Español de Oceanografía, C.O. Vigo, Subida a Radio Faro, 50, 36390 Vigo, Spain |
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| Abstract: | Three univariate geostatistical methods of estimation are applied to a geochemical data set. The studied methods are: ordinary kriging (cross-validation), factorial kriging, and indicator kriging. These techniques use the probabilistic and spatial behaviour of geochemical variables, giving a tool for identifying potential anomalous areas to locate mineralization. Ordinary kriging is easy to apply and to interpret the results. It has the advantage of using the same experimental grid points for its estimates, and no additional grid points are needed. Factorial kriging decomposes the raw variable into as many components as there are identified structures in the variogram. This, however, is a complex method and its application is more difficult than that of ordinary or indicator kriging. The main advantages of indicator kriging are that data are used by their rank order, being more robust about outlier values, and that the presentation of results is simple. Nevertheless, indicator kriging is incapable of separating anomalous values and the high values from the background, which have a behaviour different to the anomaly. In this work, the results of the application of these 3 kriging methods to a set of mineral exploration data obtained from a geochemical survey carried out in NW Spain are presented. This area is characterised by the presence of Au mineral occurrences. The kriging methods were applied to As, considered as a pathfinder of Au in this area. Numerical treatment of Au is not applicable, because it presents most values equal to the detection limit, and a series of extreme values. The results of the application of ordinary kriging, factorial kriging and indicator kriging to As make possible the location of a series of rich values, sited along a N–S shear zone, considered a structure related to the presence of Au. |
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