Knowledge discovery of geochemical patterns from a data-driven perspective

Abstract

We have entered the fourth research paradigm with the overwhelming availability of vast amounts of data. The processing and mining these data for a better understanding of earth systems and predicting mineral resources is challenging. This study discusses a data-driven knowledge discovery of geochemical patterns and presents a case study of geochemical data processing from a data-driven perspective. We employed local indicators of spatial association (LISA), principal component analysis (PCA), and deep autoencoder network (DAN) procedures to explore spatial association of geochemical patterns, extract elemental associations, and detect geochemical anomalies related to AuSb mineralization in the Daqiao district, Gansu Province, China. The results indicate the following: (1) both Au and Sb, and Pb and Zn have a close spatial correlation, indicating genetic connections among them; (2) the elemental association of Au, Sb, As, Hg and Ag can be adopted as a geochemical signature for the discovery of AuSb polymetallic mineralization in the study area; and (3) the geochemical anomalies identified by DAN exhibit a strong spatial relationship with locations of known mineral deposits and can provide a significant clue for further mineral exploration in this district. These findings indicate that data-driven procedures can help in the knowledge discovery of geochemical patterns in mineral exploration. Additional efforts are required for data-driven knowledge discovery in both geochemical prospecting and mineral exploration.