A combined approach using spatially-weighted principal components analysis and wavelet transformation for geochemical anomaly mapping in the Dashui ore-concentration district, Central China

Sometimes geochemical anomalies linked with buried mineral deposits are too weak to be recognized by conventional methods. In this study, element concentration data from a stream sediment survey were subjected to a combined method of SWPCA (spatially-weighted principal components analysis) and WT (w...
Ausführliche Beschreibung

Sometimes geochemical anomalies linked with buried mineral deposits are too weak to be recognized by conventional methods. In this study, element concentration data from a stream sediment survey were subjected to a combined method of SWPCA (spatially-weighted principal components analysis) and WT (wavelet transformation) to derive a geochemical anomaly model for epithermal Au deposits in the Dashui ore-concentration district. The SWPCA was applied as a data integration method to extract information related to mineralization in the geochemical data. The WT was applied as a powerful tool for recognizing mineralization related anomalies in a complex geochemical field and for enhancing weak anomalies from background. The SWPCA–WT geochemical anomaly model indicated high favorability values for the known mineral occurrences and out-performed PCA–WT and SWPCA models. The SWPCA–WT geochemical model generated in this study provides a robust guide for further gold exploration in the Dashui ore-concentration district. Ausführliche Beschreibung