## 1 Introduction  
For regional mineral exploration, geochemical exploration is an intuitive and effective approach. In China, between 1981 and 2005, the Ministry of Geology and the Ministry of Land and Resources, based on the \"The Regional Geochemistry National Reconnaissance (RGNR)\", identified 58,788 anomalies across the country. These anomalies led to the discovery of 3349 mineral deposits, playing a significant role in the discovery of mineral deposits in China [1, 2, 3, 4]. Geochemical exploration for mineral prospecting requires the study area to be divided into standard sizes of different scales, such as 1:250,000, 1:200,000, and 1:50,000 [5]. The work between these different map sheets may be carried out by different companies, at different times, and in different laboratories, which inevitably leads to \"shift\" effects due to systematic errors. This not only affects the visual appeal of the map sheets but also introduces errors in the judgment on the background areas and prediction of target areas [6; 7].  
To address the \"shift\" effect of geochemical exploration data between different map sheets, many researchers have proposed various leveling methods suitable for different situations. For example, [PERSON] and [PERSON] [6] proposed a method to calculate the linear relationship between two survey areas using percentiles, and tested it on Mo elements in stream sediments. [PERSON] [8] proposed a data leveling method based on compositional data analysis. [PERSON] [7] also applied linear regression equations to perform leveling of the element Ni in soil in the Wallonia region. Additionally, [PERSON] et al. [9] compared the effectiveness of several common leveling methods, including adjustment methods, normalization methods, least squares methods, and median transformation methods. However, these existing methods exhibit varying degrees of limitations. For instance, methods relying on linear regression or percentiles may not adequately account for the inherent compositional nature of geochemical data, where elements are interrelated and subject to the closure effect. Similarly, normalization or median transformation methods might obscure subtle geochemical anomalies, especially in areas with highly heterogeneous geological backgrounds where element distributions can vary significantly even within a single map sheet. A critical comparison reveals that no single method is universally optimal; their effectiveness often depends heavily on the specific characteristics of the dataset, such as the strength of compositional effects and the degree of geological heterogeneity.  
The purpose of these data processing methods is to eliminate the shift effect at map sheet boundaries. However, there are several important points to note: First, the leveling process is generally based on statistical principles, so the data should roughly follow a normal distribution before processing. Second, the leveled data still belong to compositional data, which inherently contains the \"closure effect\". This may lead to pseudo-correlation between geochemical elements, causing the data analysis results to become unstable. Third, most of the leveling methods perform calibration on the entire map sheet as a unit. However, if there are significant geochemical background differences between map sheets, this method may not be applicable.  
Therefore, in this study we processed the raw data through compositional data transformation, which could account for the inter-element relationships where the increase or decrease in one element's concentration might be related to changes in others, thereby eliminating the skewed distribution and closure effects inherent in such data. Then, we compared the leveling effects of the contrast return method (CR), boundary leveling method (BL), and multi-scale correction method (MSC), selecting the most suitable leveling method to provide reference for future processing of similar areas or data.