Geochemical lithogenes have been successfully applied as an innovative concept in the field of composition classification and source traceability of geological materials recently. This paper introduces the background of the development of geochemical genes and the construction and application of LG01 and LG03 lithogenes. Based on LG01 and LG03, the LG_CR classification and provenance are applied and verified on a weathering profile, ten gully sedimentary profiles and regional stream sediments in the Wanquan area of Zhangjiakou city, Hebei province, China. The geochemical lithology of the weathering profile shows a gradual variation from basic-like in the bottom rock to acidic-like at the upper soils compositionally with heterogeneity. The classification results on 10 sedimentary gully profiles (each with five samples) indicate that soils at the bottom of the gully system are dominated with 11 types of LG_CR materials, while the top materials are made up of 21 types, reflecting the mixing of the upstream soils. The results of stream sediments from a regional geochemical survey with a scale of 1:200,000 in this area illustrate that the classification results of LG_CR on stream sediments are basically consistent with the petrological results derived from regional geological mapping. Therefore, LG_CR can be used not only as an effective tool for classification and traceability of geological materials but also has great potential in lithological mapping in petrological-overburdened areas.

The geochemical background value of chromium (Cr) serves as a pivotal factor in environmental assessments and mineral exploration endeavors. Traditionally, geochemical background values have been determined using statistical parameters derived from dataset analysis, though this method may possess inherent limitations. In this study, we introduce a novel equation to calculate the geochemical background value of Cr, based on the premise that major elements can effectively delineate the geochemical background for trace elements. Using a dataset encompassing 791 abundance records from rocks, soils, and sediments, we derived and fitted the equation. Our findings revealed that when treating these 791 records as representative of background samples, approximately 95% of the records lie within a logarithmic error of ±0.2 from the calculated values. Based on this observation, we propose adopting a logarithmic error threshold of ±0.2 as a criterion for identifying background samples. To further validate the equation, we tested background samples from diverse regions with varying degrees of weathering. Among the 138 samples analyzed, only two exhibited background values with a logarithmic error exceeding ±0.2 from the measured values, although all samples fell within a ±0.3 error margin. We subsequently applied this equation to the orthopyroxenite and dunite zones within the Pados-Tundra complex, located in the western Kola Peninsula, Russia. According to the ±0.2 logarithmic error criterion, mildly altered orthopyroxenite samples could be deemed as background Cr samples, where certain altered orthopyroxenite samples exhibited a logarithmic error in the ±0.2–±0.3 range. In contrast, altered and mineralized samples in the dunite zone showed a significant deviation beyond the ±0.2 error range, indicating that they should be classified as anomalous Cr samples. This innovative methodology offers a refined approach for background determination and anomaly identification, thereby enhancing mineral exploration and Cr environmental quality assessments.