Application of self-organizing map (SOM) and K-means clustering algorithms for portraying geochemical anomaly patterns in Moalleman district, NE Iran

Abstract

In this paper, in order to reveal the regional geochemical patterns of regularly sampled stream sediment data, we have employed the K-means and self-organizing map (SOM) as clustering methods in the Moalleman district, northeast Iran. Initially, a set of analyzed elements of geochemical data was subjected to isometric log-ratio (ilr) transformation to address the closure problem related to geochemical data, then, ordinary principal component analysis (PCA) was utilized for recognizing the internal relations between selected elements (As, Au, Cu, Pb, Sb and Zn). Subsequently, the K-means and SOM as unsupervised clustering methods were applied based on PC1 (Cu-Pb-Zn aggregation) and PC2 (Au-As-Sb aggregation) to distinguish different populations of multi-element geochemical indicators. In this regard, Silhouette Width (SW) was implemented for computing the optimal cluster number in K-means clustering method. In the next step, due to the presence of numerous copper mineral deposits/occurrences in the study area, we opted to implement the supervised SOM on ilr-transformed values of Cu-Pb-Zn elements for delineating high anomalous zones. For this purpose, a confusion matrix based on training and out-of-bag (OOB) data was developed for the supervised SOM model and the results indicated the accuracy of 96.27% and 94.26%, respectively. Moreover, success-rate curves were used for assessing the overall performance of K-means and SOM (unsupervised and supervised) models. Experimental outcomes represented the superiority of SOM models (especially the supervised SOM) over K-means in delineating the geochemical anomaly targets which can be used as an effective and powerful tool for discovering the complex patterns among variables in exploratory geochemical data.