The Role of Factor and Regression Analysis in the Interpretation of Geochemical Reconnaissance Data

Abstract

The interpretation of exploration oriented geochemical data frequently requires the recognition of subtle features related to mineralization, from the more obvious geochemical expressions of bedrock and surface environments. A number of previous investigations have indicated the potential of various computerized interpretational procedures as aids in identifying these features in geochemical data. The present investigation was concerned with the interpretation of multi-element data from a 750 mile2 {1942.5 km2) area of the Notre Dame Bay district of Newfoundland. The area is underlain by a series of Ordovician and Silurian sediments and volcanics and intrusives overlain by glacial deposits mostly composed of glacial till. Massive sulfide mineralization including the Whalesback and Gullbridge deposits occur within the Ordovician volcanics. R mode factor analysis was employed to establish the character and distribution of the principle metal associations related to bedrock and surface environment contributing to the overall data distribution. The factor scores were regressed against the individual metal concentrations of the elements composing the respective factors, the resulting residuals of the metals reflecting the component of metal related to some sources other than those reflected by the metal associations established by factor analysis. Anomalous areas of residual copper and zinc distributions indicate the areas of known sulfide mineralization more closely than the untreated metal distributions. On this basis, anomalous areas of residual copper and zinc, unrelated to known sulfide mineralization warrant further exploration investigation. It is therefore concluded that a combination of factor and regression analysis on multi-element data from the Notre Dame Bay district of Newfoundland serves to highlight subtle though significant features in multi-element data possibly related to mineralization that were not apparent from a consideration of the untreated data.