Geochemical anomaly recognition using Shapley values and cell-wise outlier detection: a case study in the Yuanbo Nang District, Gansu Province, China

Abstract

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. In this paper, we propose to utilize the Shapley value, linked to the 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 the 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 analysing 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 relate to regional mineralization in the study area were Au-only anomalies, Au–As–Sb anomalies, As–Sb–Hg anomalies and Ag-related anomalies.