Mapping mineral prospectivity through big data analytics and a deep learning algorithm

Abstract

Identification of anomalies related to mineralization and integration of multi-source geoscience data are essential for mapping mineral prospectivity. In this study, we applied big data analytics and a deep learning algorithm to process geoscience data to identify and integrate anomalies related to skarn-type Iron mineralization in the southwestern Fujian metallogenic zone of China. Based on the geological setting and environment for the formation of skarn-type Iron mineralization, 42 relevant variables, including two geological, one geophysical, and 39 geochemical variables, were analyzed and integrated for detecting anomalies related to mineralization using a deep autoencoder network. The results indicate that the mapped prospectivity areas have a strong spatial relationship with the locations of known mineralization and demonstrate that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping.