Abstract

Mine tailings management poses a major challenge, with up to 99% of the mined material remaining as finely ground residues. This study analyzes a SERNAGEOMIN database from 653 Chilean tailing deposits using a multivariate framework that integrates completeness assessments, descriptive statistics, and hierarchical clustering on log-transformed and standardized chemical concentrations of 56 elements in order to identify dominant geochemical patterns. This study aims to provide an integrated and systematic interpretation of the Chilean database, the most comprehensive public dataset on mine tailings in Chile. The results reveal four distinct geochemical profiles: (i) silicate copper tailings, rich in Cu and associated with a SiO2-Al2O3 matrix; (ii) Zn-Pb-Cd-As polymetallic tailings, with the highest concentrations of heavy metals and rare earth elements (REEs), representing both high environmental risk and potential economic value; (iii) carbonate-matrix tailings (CaCO3 and limestone), characterized by high CaO and loss of calcination (LOI) but low trace metal contents, suggesting buffering potential against acid mine drainage (AMD); and (iv) clay-rich tailings (kaolin and Au-Cu-Au), marked by high Al2O3 and anomalous Co enrichments, indicating unexploited potential for critical metal recovery. These profiles support applications such as their use as source signatures in receptor models and the classification of tailing deposits lacking geochemical information.