## 1 Introduction  
Spatial autocorrelation analysis focuses on the similarity of attributes, as well as spatial similarity between one geological entity and adjacent entities. The spatial distribution of concentrations of elements can be regarded as a numerical field with a continued spatial coverage, which can be characterized by using spatial autocorrelation among different elements. [PERSON] and [PERSON] (2009) stressed that the nonrandom characteristics and spatial structure of geochemical data depend on the concentration field [1]. Analysis of the concentration field includes comparison of samples to recognize anomalies and using the spatial correlation among elements to explain geochemical processes. Geological interactions between elements result in mutual influence and restriction. Therefore, it is necessary to consider spatial auto- and cross correlation in geochemical studies. The concentrations and spatial association of different elements are usually related to parent lithostrata. Therefore, it is of great significance to study the distribution, enrichment, and relationships among different elements to understand regional magmatism and ore-forming process [2]. [PERSON] (1970) proposed the first law of geography: everything is related to everything else, but near things are more related than distant things [3]. The measurement of spatial autocorrelation includes global and local indicators. Global indicators reveal the spatial pattern of the whole region and reflect global characteristics. In contrast, local indicators measure the relationship between each location and its neighbors to reveal more detailed local spatial patterns. Global metrics include Moran's I [4] and Geary's C [5]. Improvements in spatial theory and statistical tests have made Moran's I and Geary's C the most widely used global indicators [6; 7; 8]. Based on Moran's I, [PERSON] (1981) also proposed a simple spatiotemporal autocorrelation indicator form, \(I_{s-t}\)[8]. [PERSON] and [PERSON] (1992, 1995) proposed global G statistic and local G* statistic [9; 10]. [PERSON] (1995) developed local indicators of spatial association (LISA), including local Moran's I and local Geary's C [11]. [PERSON] and [PERSON] (2007) proposed the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics [12]. [PERSON] (2019) extended the application of the local Geary's C statistic to a multivariate context. According to the characteristics of experimental data, each local autocorrelation indicator has its advantages and disadvantages [13]. Spatial autocorrelation indicators have been used in the fields of environmental science, regional economy, identification of diseases and mortality, and detection of geochemical anomalies [14; 15; 16; 17; 18]. The spatial pattern of the concentration field is caused by different geological processes [19]. The concentration field reflects the migration and spatiotemporal distribution of various elements. Therefore, both the spatial characteristics of a single element and the spatial relationship among multiple elements need to be considered. For a long time, the identification and evaluation of geochemical anomalies has been a key issue in the field of geochemical exploration [20; 21; 22]. A geochemical anomaly is the enrichment or dilution of elements. The enriched area often has high mineral resource potential [23; 24]. Geologists use the spatial pattern to distinguish an anomaly from the background. For many years, various statistical methods, such as mean \(\pm\) 2 \(\times\) standard deviations [25], probability graphs [26], univariate analysis [27], multivariate analysis [28; 29], logistic regression [30; 31], weights of evidence [32; 33; 34], fractal/multifractal models [35; 36; 37], and geostatistics [38; 39], have been used to identify geochemical anomalies. In recent years, machine learning methods have been used in geological prospecting.