Transported soils cause difficulties in the identification of geochemical anomalies. It has been demonstrated that the joint application of Local Singularity Analysis (LSA) and Principal Component Analysis (PCA) can identify geochemical anomalies effectively, especially in regolith-covered areas. However, more convincing evidence is needed to explain the reasons for this. In this study, a soil profile overlying several mineralized veins cutting through the bedrock was analyzed in-situ using a portable X-ray fluorescence spectrometer. The patterns of two mineralization-related elements, Cu and Mo, were analyzed. The results revealed that the element concentrations of the soil sharply decreased as the distance from the bedrock increased, and this relationship can be described by a power law model. The LSA enhanced several vein-like anomalies corresponding to the mineralization veins in the bedrock, and the presence of vertically elongated weak anomalies in the soil indicates the migration of ore elements originating from the underlying bedrock through the soil. The statistics show that the patterns of the Local Singularity Index (LSI) are stable at different depths and in different media, whereas the concentration patterns are not. In addition, the mineralization-related elements have a higher correlation coefficient for the LSI than for the concentration. Since a previous simulation study determined that a mineralization indicative first principal component prefers that the variables have a close relationship and that the variables have similar patterns in different geological objects, the patterns discovered in this study explain why LSA is effective in identifying geochemical anomalies, especially when combined with PCA.

Supplementary material: the high-resolution photo, the element concentration data and the lithologic data of the profile are available at https://doi.org/10.6084/m9.figshare.c.5957122.

Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis

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