Geochemical pattern recognition has long been of interest for geologists to reveal geochemical anomalies associated with mineralization. In regional-scale exploration, geochemical anomalies are derived conventionally from stream sediment samples and processed in the form of vectors, resulting in row-wise outliers. However, geochemical anomalies derived through various means of pattern recognition have shown their limits in depicting complex geochemical distributions. This paper proposed to utilize the Shapley value linked with Mahalanobis distance (MD) and cell-wise outlier detection to facilitate the recognition of anomalous geochemical indicator elements. First, by considering the compositional nature of geochemical data, multivariate outliers are detected based on MD in isometric log-ratio coordinates. Secondly, to quantify the contributions of individual elements to the outlyingness of an outlier, Shapley values are used to express the MDs of data as outlyingness contributions of single elements. Finally, cell-wise outlier detection is introduced to examine and quantify the outlyingness of each cell in a geochemical data matrix. The outlying cells serve as criteria for further recognition of element associations. By analyzing the Shapley values of individual elements and the outlying cells in a geochemical data matrix, more information contained in multivariate outliers can be recognized. Using this proposed methodology, the element associations that related to regional mineralization in the study area were: Au-only anomalies, Au–As–Sb anomalies, As–Sb–Hg anomalies and Ag-related anomalies.

This content is PDF only. Please click on the PDF icon to access.
You do not have access to this content, please speak to your institutional administrator if you feel you should have access.