Application of the Expectation-Maximization Clustering Method for Identifying Li Geochemical Anomalies in Stream Sediments in Southeastern Hunan Province, China

The identification of lithium (Li) geochemical anomalies is crucial for the exploration of Li mineral resources. However, variations in lithological backgrounds in lithologically complex regions often hinder the accurate identification of these anomalies. In this study, we employ an unsupervised Expectation-Maximization (EM) clustering algorithm to tackle this issue. Using 1:200,000 scale geochemical data from 2559 stream sediment samples in Chenzhou, Hunan Province, China, we selected seven major elements—SiO2, Al2O3, Fe2O3, MgO, CaO, Na2O, and K2O—as clustering indicators. This approach allowed us to classify the samples into six distinct groups, significantly reducing the influence of lithological background on the detection of Li anomalies. After applying the 3σ technique to eliminate 122 outliers and conducting Z-score normalization on Li concentration data within each group, Li anomalies were identified using a uniform threshold of the mean + two standard deviations. The results indicate that the EM clustering method effectively suppresses pronounced yet spurious anomalies in high-background areas where granitic intrusions are present, accounting for approximately 0.6% of the total study area, while simultaneously uncovering subtle but significant anomalies in low-background regions characterized by slightly metamorphic and siliceous rocks, accounting for approximately 1.7% of the total study area. This approach substantially improves the reliability of anomalies, offering a robust tool for Li exploration in lithologically complex regions.