## 4 Discussion
We set the binarization threshold as the 60% quantile: 0.10515. Some ore spots, e.g., fluorite, Nb-Ta, etc., are removed from this figure because of the absence of relevant geochemical data. The 1:50,000 soil/stream-sediment geochemical data was provided by the Development Research Center, China Geological Survey. To ensure consistency, the 1:200,000 multiresolution segmentation result shown in Figure 2 is reused to generate the 1:50,000 anomaly score map. Other legends in this figure are consistent with those in Figure 11 if not specified. better. Figure 17 is such an example: when the threshold is set as the 70% quantile and the scale parameter is set as 2, 81.4% of the I-series mineral spots can fall within the corresponding buffered-anomalous-area which accounts for <45% of the study area, and 80.9% of the S-series ore spots can be included. By contrast, if the scale parameter increases to 3, only 72% of the I-series ore spots and 70% of the S-series ore spots can be included in the relevant anomalous areas. Given the above, in order to balance the recognition rate and anomalous area, it is better to use a smaller scale parameter to segment the multivariable geochemical image. Figure 17: (**upper**) the object-based anomaly score map for I-series elements with relevant ore spots (red squares) overlaid on it. (**lower**) the object-based anomaly score map for S-series elements with relevant ore spots overlaid on it. The image objects with larger scores are represented by brighter tones, while the image objects with smaller scores are represented by darker tones. Here we set the binarization threshold as the 70 th percentile value, and the regions delineated by green lines are the anomalous regions with a 3-pixel buffer zone, which account for approximately 44.375% of the total area. Sixth, as our OGE architecture was developed as an updated version of GAUGE by combining OBIA and GNN (GAT & GCN) into a singular network, so it is necessary to compare the performance between them. As shown in Figure 19, which is generated by using the geochemical dataset consisting of all 18 ore-forming elements as input to GAUGE, we found that the spatial distribution patterns of the anomalous areas are generally accorded with those in Figure 11, but more details have emerged; if setting the binarization threshold as the 50% quantile, there are more than 19 ore-spots falling outside but being close to the anomalous regions; if setting the threshold as the 70% quantile, there are 25 missed ore-spots. As far as the recognition rate is concerned, the performance of GAUGE is no better than OGE. Nevertheless, even the threshold is set as the 70% quantile, most of the missed ore spots could fall within the 2 km (i.e., the controlling distance of one sampling point) buffered anomalous regions, and the ore- spot recognition rate approaches 100%. However, the resulting anomalous area will increase to >75% of the total area. In addition, the within-object statistical analysis is not supported by GAUGE. Given the above, our OGE performed better in anomaly detection: in Figure 17, more than 80% of the known ore spots were predicted in the buffered anomalous area accounting for less than 45% of the total area. In fact, both GAUGE and OGE were designed to extract both the spatial features and compositional relationships of geochemical variables collected at irregularly distributed locations. For OGE, such irregularity is implemented by the multiresolution segmentation whose output is a series of irregularly distributed centroids in irregularly shaped image objects [6], which may reflect the variation of geochemical background across the space. On the other hand, as shown in Figure 19, the sampling grid is relatively regular, and very few geochemical sampling points are missing in this area. This implies that GAUGE could not capture the practical spatial distribution characteristics of geochemical variables, which reflects complex geologic processes (i.e., mineralization). That is why the visual performance of GAUGE is not as good as expected.