Identification of multivariate geochemical anomalies using spatial autocorrelation analysis and robust statistics

Abstract

Multivariate geochemical anomaly identification is one of the most important tasks in exploration geochemical data analysis. This paper proposes a new approach to identify multivariate geochemical anomalies which accounts for the degree of spatial autocorrelation among observations. This method is a combination of Robust Mahalanobis Distance (RMD), spatial autocorrelation analysis and robust statistics. The Minimum Covariance Determinant (MCD) estimator-based RMDs were first used to reduce the dimension of geochemical data sets. Spatial correlogram and local Moran’s I statistics were then applied to determine the spatial variability (spatial scales) and to measure the degree of spatial autocorrelation at global and different local scales, respectively. Negative and positive anomalies were finally separated from a set of local Moran’s I values using robust statistics, the MEDIAN ± 1.5*IQR (IQR: interquartile range) rule. We illustrated the proposed method by using a data set of 1842 stream sediment samples associated with Cu-Au-Mo and Sn mineralizations in the Jiurui ore district (Jiangxi province, southeastern China). The results showed that (i) the maximum spatial variability of RMDs were 12 km, in which strong positive spatial autocorrelation was detected at the first three spatial scales ranging from 0 to 6 km; (ii) both of negative and positive multivariate geochemical anomalies were strongly correlated with 13 known Cu-Au-Mo and Cu-Mo deposits, especially at spatial scales where high degrees of positive spatial autocorrelation were measured; (iii) weak anomalies associated with a magmatic-hydrothermal related Sn and polymetallic mineralization were successfully identified in the Zengiialong and Jianfengpo Sn deposits, which were not detected in previous studies. We demonstrate the combination of spatial autocorrelation analysis and robust statistics allows for improved identification of significant multivariate geochemical anomalies of the deposit-type sought from stream sediment geochemical data.