Effectiveness of LOF, iForest and OCSVM in detecting anomalies in stream sediment geochemical data

This paper compares three unsupervised machine-learning algorithms – local outlier factor (LOF), Isolation Forest (iForest) and one-class support vector machine (OCSVM) – for anomaly detection in a multivariate geochemical dataset in northeastern Iran. This area contains several Au, Cu and Pb–Zn mineral occurrences. The methodology incorporates single-element geochemistry, multivariate data analysis and application of the three unsupervised machine-learning algorithms. Principal component analysis unveiled diverse elemental associations for the first seven principal components (PCs): PC1 shows a Co–Cr–Ni–V–Sn association indicating a lithological influence; PC2 shows a Au–Bi–Cu–W association suggesting epithermal Au mineralization; PC3 shows variability in Zn–V–Co–Sb–Cu–Cr; PC4 shows a Au–Cu–Ba–Sr–Ag association indicating Au and polymetallic mineralization; PC5 reflects Zn–Ag–Ni–Pb related to hydrothermal mineralization; and PC6 and PC7 show element associations suggesting epithermal and intrusive-related polymetallic mineralization. It was found that OCSVM performed slightly better than LOF and iForest in detecting anomalies associated with known Cu occurrences, and it successfully delineated dispersion from all known Au occurrences. LOF outperformed iForest and OCSVM in identifying all four Pb–Zn occurrences, and the three methods substantially limited the areas of the anomaly class. The analysis showed that LOF produced a less cluttered anomaly map compared to the isolated patterns in the iForest map. LOF was accurate in identifying anomalies associated with Au–Pb mineralization, while iForest detected anomalies associated with Pb–Zn–Cu occurrences and neighbouring Pb–Zn occurrence. OCSVM performed similarly in the northern and western areas but displayed unique discrepancies in the SE and west by detecting anomalies associated with two Cu occurences and a Pb–Cu occurrence. This study examined the influence of contamination fraction on detection of geochemical anomalies, revealing a noteworthy rise in the count of mineral occurrences delineated by anomalies when the contamination fraction increases from 5 to 10%. However, even with a 35% contamination fraction, some Cu occurrences remained outside the anomaly category, indicating potentially overlooked geochemical signals from mineral occurrences due to sampling schemes.