Identification of geochemical anomalies related to mineralization: A case study from porphyry copper deposits in the Qulong-Jiama mining district of Tibet, China

Abstract

Geochemical exploration data plays a vital role in mineral prospectivity mapping (MPM) for discovering unknown mineral deposits. In this study, compositional balance analysis (CoBA), unsupervised and supervised learning are collaboratively used to improve the practice of identification of geochemical anomalies related to mineralization in the Qulong-Jiama mining district of Tibet. For CoBA, five balances of geochemical elements were constructed by the sequential binary partition (SBP) technique, facilitating the interpretation of geochemical/geological processes and contrasting with geochemical anomalies generated by unsupervised and supervised learning. The iterative self-organizing data analysis techniques algorithm (ISODATA) and isolation forest (iForest) are used for preprocessing the geochemical data and therefore optimizing the appropriate training sets (e.g., positive and negative training samples) for supervised learning. Different training datasets including locations of known mineralization, randomly selected positive samples from resulting clusters by ISODATA and outliers by iForest are fed to the support vector machine (SVM) and random forests (RF) algorithm. The results of high AUC values indicate that supervised learning can effectively delineate prospective areas both with SVM and RF, however, the excessive large area of high-prospectivity and a bias toward known mineralization makes the outcomes of MPM unpractical. Nevertheless, a hybrid of the CoBA, unsupervised learning and supervised learning could alleviate such situations and provide insights into MPM using the geochemical data.