Semi-automated geological mapping and target generation from geochemical and magnetic data in Halkidiki region, Greece

Abstract

This study focuses on using multivariate analyses to generate semi-automated geological maps and exploration targets associated with porphyry Au-Cu mineralization within the Kassandra mining district, Greece. We use principal component analysis (PCA) and self-organizing maps (SOM) to reveal variations in geochemical and magnetic signatures within the input datasets. We visualize the results as pseudo-geological maps reflecting the associated geological processes and their end products. In specific, we utilize the potential of these two methods through an integrated interpretation and comparison of the results. We test the validity of the unsupervised PCA- and SOM-derived lithological and prospectivity models by comparing them with existing geological observations and interpretations. The results of this investigation show that both PCA and SOM are able to reproduce the key features of existing geological observations within the study area, but more importantly, also provide useful information that can be used to recognize prospective geological units and exploration targets from previously unknown locations.