Knowledge extraction in geochemical data by using self-organizing maps.

In this contribution we apply a self organizing map algorithm to And groups or clusters in two different multivariable geochemical datasets. The first dataset includes whole rock chemical data of four different island arc volcanic rock types: basalts, andesites, dacites and rhyolites. The second dataset includes compositional data of clinopyroxenes from sixteen different volcanic rock types of mainly mafic composition. The outcomes of the method clearly reveal the cluster structure of both datasets. The relevant factors responsible for the cluster structures can also be visualized and meaningful correlations between relevant discriminating factors identified. For the island arc dataset the results show that there are systematic changes of major element concentrations between the different island arc volcanic rock types. These chemical changes resemble the differentiation trend from basalts to rhyolites. The results also show that for this dataset the cluster structure can be improved by using the log-ratio values rather than the not normalized major element data. The results from the analysis of the pyroxene dataset show that the composition of clinopyroxenes clearly reflects the chemical differences and similarities that exist between different basaltic magma types.