Multi-Scale Geochemical Data Analysis and a Subzone Robust Background Method (SRBM): A Case Study from the Bedrock Terrain of the Northern Margin of Qaidam Basin, Northern Tibetan Plateau

The arid-alpine landscape on the northern margin of the Qaidam Basin, northern Tibetan Plateau, presents a challenge for conventional geochemical background modeling. Leveraging a unique multi-scale geochemical dataset (1:200,000, 1,195 samples; 1:50,000, 18,855 samples; 1:25,000, 35,675 samples) from the Qinghai Geological Exploration Fund, this study systematically evaluates scale effects on background estimation. We compared the performance of four prevalent methods—Iterative Exclusion (IE), Median Absolute Deviation (MAD), Exploratory Data Analysis (EDA), and the Concentration-Area (C-A) fractal method—and found that a multi-method approach effectively balances the comprehensiveness and precision of the background modeling framework. To address the spatial heterogeneity of backgrounds caused by complex geology, we propose a novel Subzone Robust Background Method (SRBM). This method calculates robust median background values within distinct geological units and generates an adaptive background field through area-weighted fusion. In the Banhongshan area, application of the SRBM precisely calibrated the gold background to 0.47 ng/g, successfully identifying two concealed gold anomalies obscured by traditional methods; one anomaly coincides perfectly with known industrial orebodies. This research demonstrates that high-density sampling (1:25,000) significantly enhances the signal-to-noise ratio for chalcophile elements in arid-alpine terrains. The SRBM, by integrating geological knowledge with robust statistics, provides a powerful and reliable tool for weak geochemical signal extraction in both mineral exploration and environmental assessment.