Multivariate modelling of geochemical and geophysical exploration data

Abstract

Esbensen, K., Lindqvist, L., Lundholm, I, Nisca, D. and Wold, S., 1987. Multivariate modelling of geochemical and geophysical exploration data. Chemometrics and Intelligent Laboratory Systems , 2: 161–175. We describe an integrated data-analytical strategy for overburden geochemical prospecting, based on principal component analysis (SIMCA), partial least-squares regression (PLS) and geostatistics (kriging). The strategy extracts and integrates relevant prospecting information existing in multivariate data from several geological sample media. Data-analytical modelling with subsequent geographical interpolation and plotting compresses the relevant geological information into a manageable number of maps. The procedure defines a multivariate divide between the background and anomalies and combines single variables into geologically useful components. Classification of all survey samples or new data with respect to background models emphasizes anomalies in their geographical context, giving quantitative indications of target areas for follow-up exploration activities. To further improve a model of an area, the strategy also optionally involves integration of geochemistry with petrophysical and other geophysical and geological data. The emphasis is on characterizing the underlying bedrock into areas with similar chemical and petrophysical properties, resulting in a digital geological map of the subsurface local geology. This output is designed for further image analysis etc. A glacial-overburden prospecting campaign from Northern Sweden illustrates the various elements of the present approach.