## 1 Introduction  
Regional geochemical surveys are an important part of geoscience investigations in both environmental and mineral exploration studies [1]. By discrete data gridding, the original X-Y-Z (longitude-latitude-element content) regional geochemical data can be converted into a big matrix, which is analogous to a remote sensing (RS) image: e.g., a matrix element vs. an image pixel, element content vs. the reflectance value or digital number [2], a multi-element dataset vs. a multiband image, delineating geochemical anomalies vs. classifying ground objects, etc. Intuitively, some algorithms developed for processing the remotely sensed data may be equally applicable to geochemical data analysis. The object-based image analysis (OBIA) [3] is such an example. In the RS field, there has been a very rapid growth in the use of OBIA ever since its documented introduction in late 1990s [3]. The first step of this method is the grouping of spatially contiguous pixels with similar spectral/textural characteristics into meaningful image objects, which is a process often termed \"segmentation\". An object is usually composed of a group of pixels that are spatially continuous and have high spectral homogeneity, and the shape of an adaptive object is usually consistent with the shape of a ground target [4]. Once the objects are formed, the next step is to assign appropriate labels to them by using a supervised or unsupervised classifier [5]. The object-based approach is a widely accepted solution to process high spatial resolution RS data [6], and obviously, it applies as well to analyzing regional geochemical dataset if the X-Y-Z matrix is plotted as an image. However, current geochemical data analysis conducted in the literature is still on a per-point basis [7], and every sampling point (matrix element) is treated as an individual unit and is processed without any textural, contextual, neighborhood, and shape consideration. Therefore, except identification and separation of the anomalies from background, traditional geochemical analysis rarely contributes new insight [7; 8; 9]. In practice, the geological object, e.g., sedimentary stratum, tectonic line, igneous intrusion, etc., is usually the minimum mapping unit required for regional geological analysis [9]. Likewise, it is feasible and better to use the geochemical objects produced by OBIA to do regional geochemical analysis. According to [10], OBIA can be carried out in two ways: (1) the object-level analysis: most of the current object-oriented analysis approaches rely on object-level summary statistics, such as median, mean, and standard deviation of the pixel values within each segment. Such summary measures provide one value per band for each segment to describe its data central tendency or dispersion [11]. (2) The within-object analysis. The pixel values within an object usually have their own distribution pattern--unimodal Gaussianity or multiple peaks [3], and it reflects the internal structure characteristics of the object. In theory, these data-analysis/-mining techniques apply as well to analyzing regional geochemical datasets characterized by multi-source, multi-stage and multi-genesis [7], and a large amount of geological information can thus be rediscovered, not just anomaly and background. Moreover, OBIA is often performed at multiple levels of scale to capture variably sized image objects, which may help with discriminating between classes that are differentiable by size (for example buildings versus forests) [12]. At a given scale parameter, homogeneous areas (background) result in larger objects, and heterogeneous areas (targets/anomalies) result in smaller objects. The larger the segmentation scale, the larger the segmented objects grow, and vice versa. Obviously, such a multi-scale design will help to establish a better understanding of the regional geochemical processes at different scales. In OBIA-based image analysis, the standard practice is to conduct \"multiresolution segmentation + machine learning-based classification\" [5].