## 5 Conclusions  
The main contribution of this article is fourfold as follows: (1) to the best of our knowledge, this is the first time that the OBIA framework has been applied in regional geochemical prospecting; (2) a novel GNN architecture (OGE) has been designed to extract multi-elemental geochemical anomalies which can locate most of the known ore spots, no matter what their scales and mineral species; (3) for the first time the within-object multi-elemental statistical analysis has been conducted to modify the anomalous regions delineated by object-level summary statistics; and (4) most studies on separation of geochemical anomalies in the literature focus on a singular mineral species, while our study attempts to delineate the all-mineral-species anomalies. Taking the 1:200,000 stream-sediment geochemical survey data in the Bayantala-Mingantu district, North China as case study, our findings indicated that:  
(1) Most of the ore spots falling outside the delineated anomalous regions occur nearby them. (2) Due to the low resolution of the geochemical data at hand and its weak ability in detecting the micro-mineralization outcrops, the anomalous area produced by OGE is relatively large and mainly reflects the regional diagenesis. (3) The within-object statistical analysis based on handcrafted features, e.g., D2 and J, has a limited effect on reducing the anomalous target area. (4) OGE can be guided to focus more on regional mineralization, rather than diagenesis, by using fine-scale geochemical data as input. (5) Smaller segmentation scales can greatly improve the application of OGE. Finally, more than 80% of the known ore spots were predicted in less than 45% of the study area. (6) OGE is designed to extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed centroids in irregularly shaped image objects, and it performs slightly better in anomaly detection than other convolutional autoencoder models such as GAUGE. Future research will focus on improving the OGE in the following respects: (1) to alleviate the spatial heterogeneity of geochemical backgrounds, it is better to conduct the mean removal operation within every image object, rather than removing the global mean values for each element. (2) The optimal segmenting scale can be determined by using the ESP (estimation of scale parameter) tool proposed in [39]. (3) The possible approaches  
Figure 19: Anomaly map obtained by GAUGE. (**left**): threshold = the 50% quantile; (**right**) threshold = the 70% quantile. Note: the colored dots represent different geochemical sampling points; yellow dots denote anomalies and blue dots denote the background. All the known ore-spots (red squares), no matter what their scales and mineral species, are overlaid on the anomaly maps. The anomaly score maps are generated by using the geochemical dataset consisting of all 18 ore-forming elements as input to GAUGE. of integrating multiple datasets (e.g., remote sensing data and geophysical data) in the framework of OGE will also be explored. (4) Improve the within-object statistical analysis method, so that those strong but false or meaningless anomalies can be eliminated, while retaining those weak but important or meaningful anomalies. Conceptualization, B.Z. and D.Z. ; methodology, B.Z. and R.Z. ; software, B.Z. ; validation, P.T. and H.W. ; formal analysis, B.Z., H.W. and P.T. ; investigation, B.Z. and Z.L. ; resources, Z.L. ; data curation, Z.L. ; writing--original draft preparation, B.Z. ; writing--review and editing, D.Z. and R.Z. ; visualization, B.Z. ; supervision, D.Z. ; project administration, R.Z. and Z.L. ; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript. This work was financially supported by the project of \"Study of the Ore-Forming Regularity and Ore Prediction for Key Metallic Deposits in the Bayantala-Mingantu District, Inner Mongolia, China\" under Grant 2020-KY03. Not applicable.