title: A multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration.
abstract: This paper describes a multivariate geostatistical methodology to delineate areas of potential interest for future sedimentary gold exploration, with an application to an abandoned sedimentary gold mining region in Portugal. The main challenge was the existence of only a dozen gold measurements confined to the grounds of the old gold mines, which precluded the application of traditional interpolation techniques, such as cokriging. The analysis could, however, capitalize on 376 stream sediment samples that were analyzed for twenty two elements. Gold (Au) was first predicted at all 376 locations using linear regression (R2=0.798) and four metals (Fe, As, Sn and W), which are known to be mostly associated with the local gold's paragenesis. One hundred realizations of the spatial distribution of gold content were generated using sequential indicator simulation and a soft indicator coding of regression estimates, to supplement the hard indicator coding of gold measurements. Each simulated map then underwent a local cluster analysis to identify significant aggregates of low or high values. The one hundred classified maps were processed to derive the most likely classification of each simulated node and the associated probability of occurrence. Examining the distribution of the hot-spots and cold-spots reveals a clear enrichment in Au along the Erges River downstream from the old sedimentary mineralization.
authors: BioMedware, PO Box 1577, Ann Arbor, MI 48106, USA., phone: 734-913-1098, fax: 734-913-2201.
authors: P
authors: P
authors: Goovaerts
authors: CIGAR - Geo-Environmental and Resources Research Center, FEUP, Oporto and Polytechnic Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal.
authors: Teresa
authors: T
authors: Albuquerque
authors: CIGAR - Geo-Environmental and Resources Research Center, FEUP, Oporto and Polytechnic Institute of Castelo Branco, 6001-909 Castelo Branco, Portugal.
authors: Margarida
authors: M
authors: Antunes
keywords: accuracy plots; cluster analysis; linear regression; sequential indicator simulation; soft indicators