## 1 Introduction
Actually, over the last few years, machine learning techniques have become an essential tool to advance different branches of science and engineering, including geochemical anomaly recognition. For example, a hybrid machine learning method was proposed in [13], which is based on combining K-Nearest Neighbor Regression and Random Forest Regression to predict Pb and Zn grades in the Irankuh Mining District (IMD), Iran. Reference [14] goes deeper: it trained four regression machine learning algorithms, i.e., K neighbor regressor, support vector regressor, gradient boosting regressor, and random forest regressor, to build a hybrid model to predict Pb and Zn grades of IMD. After that, the multifractal model [15] was used to classify Pb-Zn anomalies. Despite the success of the examples in the literature, few studies have explored the advantages of introducing traditional machine learning algorithms into OBIA-based geochemical prospecting. At the same time, over past years, deep learning has yielded impressive results in image analysis, e.g., the land cover mapping. In particular, neural networks with FCN (fully convolution networks) structures are widely used in image analysis tasks [16]. In recent years, there has become an increased focus on the combination of OBIA and FCN. For example, in [17], the object-based convolutional neural network (OCNN) was proposed for urban land use classification using high-resolution RS images. Rather than pixel-wise convolutional processes, OCNNs rely on segmented objects as their functional units, and then, FCNs were used to analyze and label objects such as to partition within-object and between-object variation [10]. Reference [18] presented a multiscale OCNN framework for large-scale RS land cover classification, in which FCNs pretrained at multiple scales were applied for final classification. Additional examples which adopt OCNN as the basic image analyzer can be found in [19; 20; 21; 22]. Although OCNN approaches have achieved astonishing performance, they require an enormous amount of training data [23]. In fact, supervised OCNN are unsuitable for regional geochemical exploration because we cannot have a priori knowledge to make per-point or per-object annotations for multi-element geochemical data. Unsupervised learning methods, on the other hand, do not rely on labeled samples, and have become the mainstream methodology in geochemical anomaly recognition [24; 25]. GAUGE (i.e., recognition of Geochemical Anomalies Using Graph Learning) [24], One-class Support Vector Machine [25], and Autoencoders [26] are recent examples. However, there is still a research gap regarding the combination of OBIA and unsupervised deep learning, and we wonder if such a combination could produce even more exploration geochemical information and provide new insights into regional geologic process. This study mainly follows the idea of GAUGE [24]. GAUGE is a three-step procedure including: (1) building up a multi-elemental geochemical topology graph at a group of randomly located sampling points. (2) constructing a symmetric GAT (Graph Attention Network) autoencoder, namely an attributed graph encoder combined with an attributed reconstruction decoder [27]. The former is used to model both the spatial structure and compositional relationships of geochemical variables simultaneously, and the latter is responsible for reconstructing the input variables with the obtained node embeddings. (3) conduct the anomaly detection. To be specific, the Euclidean distance between pairs of original multi-elemental content values and the reconstructed background values at each sampling point are calculated as the anomaly score, and on this basis, the anomaly map is created [24].