## 4 Discussion  
In our opinion, there are six meaningful aspects around the proposed network architecture worth to discuss, which are listed in the following paragraphs. First, many factors, e.g., steep topography, the weathering and transportation of chemical compositions in stream sediment samples, surface hydrological processes, etc., can cause displacement of geochemical anomalies from the source area [7], and that is why most of the mineral spots falling outside the delineated anomalous regions occur nearby them. Inasmuch, it is highly necessary to create buffer zones for the known anomalous regions, although the total anomalous area increases accordingly. Second, Figure 15 gives the anomalous score map using the geochemical dataset consisting of seven major rock-forming elements (Fe\({}_{2}\)O\({}_{3}\), Al\({}_{2}\)O\({}_{3}\), SiO\({}_{2}\), MgO, Na\({}_{2}\)O, K\({}_{2}\)O, and CaO) as input to OGE. Here we suppose that the anomalies delineated in Figure 15 reflect the regional diagenesis. It can be seen from Figure 15 that only 6 ore-spots fall outside the anomalous regions, and the ore-spot recognition rate is as high as 91.176%, exceeding Figure 11's 88.24%. The spatial distribution of the anomalous regions in Figure 15 is also consistent with that in Figures 9-11. Obviously, owing to the strong control of bedrock geology may exert on the chemical composition of stream sediments or soil samples, the determination of geochemical anomalies can be heavily affected by the lithology background in areas with variable lithologies. That is to say, the mineralization is attached to the diagenesis, and the delineated anomalies in Figures 9-11 primarily reflects the regional diagenesis, and, to a lesser extent, the mineralization. [PERSON] et al. [36] further pointed out that, due to the lower spatial resolution, the 1:200,000 geochemical data cannot directly link to small-/medium-scaled ore occurrences, especially the micro-mineralization outcrops. Worse still, taking the image objects as basic analysis units tends to accentuate these trends rather than mitigate them. These explained why our delineated anomalous area is so large and is largely controlled by the outcropped bedrocks as displayed in Figure 1.  
Figure 15: The buffered anomalous regions (green colored patches edged with white) with all the known ore spots overlaid on it. Note that the anomalous regions are generated by using the geochemical dataset consisting of the major rock-forming elements as input to OGE. We set the binarization threshold as the 50% quantile: 0.66982. Other legends in this figure are consistent with those in Figure 11 if not specified. Third, in the within-object analysis stage, we use fractal dimension \(D_{2}\) to measure the degree of multi-elemental clustering, but its performance in terms of reducing the prospecting-target-area is not as good as expected. We also observed that: the overall distribution pattern of the multi-elemental contents of the \"pixels\" in an object is usually right-skewed, thus it can be approximately modelled by a bimodal Gaussian distribution: one population deals with the normally distributed part, and the other one deals with the long right tail--although sometimes it is multi-peaked. And then, we can separate them and obtain their shape parameters, namely \(m_{1}\) and \(m_{2}\): the mean values of two adjacent Gaussian distributions, respectively, and \(m_{1}<m_{2}\); \(\sigma_{1}\) and \(\sigma_{2}\): the corresponding standard deviations. On this basis, the separability (\(J\)) [37] is calculated as:  
\[J=2(1-e^{-\mathrm{B}}),\,J\in[0,2], \tag{9}\]  
\[B=\frac{1}{8}(m_{1}-m_{2})^{2}\frac{2}{\sigma_{1}^{2}+\sigma_{2}^{2}}+\frac{1 }{2}\ln\left[\frac{\sigma_{1}^{2}+\sigma_{2}^{2}}{2\sigma_{1}\sigma_{2}}\right] \tag{10}\]  
If the metallogenesis or differentiation occurs, the multi-elemental content values within an image object will have a small separability because of the strong feature of elemental association; otherwise, they will have a larger \(J\) value.