To efficiently delineate mineral-induced geochemical anomalies within the Hongyahuo area, principal component analysis (PCA), S-A multifractal modeling, and singularity modeling were employed to examine multi-element datasets derived from 641 soil samples collected from natural gully systems. The isometric log-ratio (ilr) transformation was implemented in conjunction with histogram and quantile-quantile plot analysis to assess and compare the multivariate statistical properties of elemental data across three formats—original, logarithmic, and ilr-transformed. The findings demonstrate the following: (1) following ilr transformation, the issue of data closure was resolved, resulting in elemental distributions more closely approximating normality; (2) PCA revealed two distinguishable elemental associations: PC1 corresponds to the Cu-Fe-Mn-Ni-Pb-Zn group, indicative of a medium- to high-temperature hydrothermal mineralization assemblage associated with Cu-Pb-Zn polymetallic mineralization linked to magmatic intrusion and fracture systems, signifying overprinted copper polymetallic mineralization events; PC2 reflects the Au-As-Sb elemental combination, associated with low-temperature hydrothermal processes indicative of Au-Sb mineralization; (3) the decomposition of the S-A model indicated that low-background and high-anomaly zones for PC1 are primarily situated within andesitic units, where nearby intermediate to felsic intrusions and structural fracture zones have likely served as sources for Cu-polymetallic mineralization; (4) the spatial distribution of the singularity index suggested that anomalous regions characterized by a PC1 singularity index α < 2 were relatively confined, offering strategic implications for mineral exploration targeting; and (5) when integrated with regional metallogenic background, three prospecting targets were identified, leading to the subsequent discovery of two copper ore bodies through anomaly validation. Therefore, this integrative analytical framework is demonstrated to be a robust approach for delineating geochemical anomalies.

Antimony (Sb) is widely utilized in modern industries and classified as a critical mineral worldwide. China possesses the most abundant Sb mineral resources globally, with over 70% of proven reserves concentrated in southern China. However, due to excessive exploitation, the lifespan of Sb reserves is less than ten years. Therefore, the knowledge of Sb needs to be urgently strengthened. Here, the effectiveness of the geochemical block method to delineate the prospective areas of Sb deposits and determine the potential resource of Sb was studied, with geochemical data covering 2.3 million km² across southern China. The statistical results showed that the median concentration of Sb in stream sediments (1.05 ppm) in southern China was 1.5 times that of whole China, indicating a significantly elevated background level of Sb in southern China. By setting the threshold value at 2.98 ppm, a total of nine distinct geochemical blocks were delineated. Remarkably, 85% and 76% of the large and medium Sb deposits were located within these geochemical blocks, respectively. Taking the block containing the Xikuangshan giant Sb deposit in Hunan Province as the model, the metallogenic coefficient of Sb is calculated to be 0.32%. The total potential Sb resources in southern China within a depth of 1000 m are estimated to be 21.939 million tons, which are primarily concentrated in the provinces of Hunan, Guangxi, Yunnan, and Guizhou. When the threshold value is set at 4.19 ppm, the predicted potential resources of Sb are 21.45 million tons in twelve blocks, which closely approximate 21.939 million tons. The estimated Sb resource in this study significantly surpasses the proven reserves. The periphery and deep areas of known Sb deposits still hold promising prospects for exploration. This study highlighted the effectiveness of employing the geochemical block method to estimate the potential resources of Sb and determined the ore-prospecting direction of Sb deposits. However, it is important to acknowledge and address the overestimation of predicted resources resulting from the high geological background.