Recognizing distribution patterns of ore-related pathfinder elements requires an understanding of the corresponding ore-forming systems and may lead to the definition of more reliable exploration targets. Given that complex ore-forming processes in complex geological terrains can result in either enrichment or depletion of ore elements, simultaneous consideration of both processes in geochemical anomaly modelling would be advantageous for defining exploration targets. Here, we adapted the “cosine similarity measure” method for pattern recognition of ore-related geochemical anomalism to facilitate simultaneous modelling of element enrichment and depletion. We tested the performance of this new method on porphyry copper exploration data from the Manzhouli belt, China, and demonstrated that our method is highly efficient with respect to recognizing orerelated geochemical signatures and, thus, useful for “vectoring” towards porphyry copper deposits and defining more reliable geochemical exploration targets.
