Identifying geochemical anomalies using a new method of Yang Chizhong-spatial scan statistic

Abstract

Identifying anomalies from geochemical data by modeling of the background and statistical evaluation of anomalies is a major concern in geochemical exploration. This study developed a novel method (namely YangScan) for extracting geochemical anomalies by using Yang Chizhong filtering and a spatial scan statistic. The Yang Chizhong filtering, a progressive filtering method on the principle of weighted moving mean of binomial coefficient, was used to construct the background whose concavity and convexity are consistent with the original data. After removing the background from the original data, a spatial scan statistic based on multidirectional optimization was developed to identify arbitrary shaped and statistically significant anomalies from the residuals. YangScan was tested on both simulated datasets and a stream sediment geochemical dataset collected in the Northwestern Jiaodong Peninsula, Eastern China. The experimental results evaluated by geology consistency, recall, and weights of evidence contrast show that YangScan outperforms the two comparision methods (i.e., the trend surface analysis method and k-nearest neighbor anomaly detector) in identifying weak and irregularly-shaped geochemical anomalies. The distribution of geochemical anomalies linked to Au mineralization identified by YangScan is highly correlated with the trending of known Au deposits and ore-controlling structures. Therefore, YangScan is a powerful tool for identifying geochemical anomalies and can provide a useful reference for mineral exploration.