## 4 Discussion
Unfortunately, like the case in Figure 13, only a moderately reduced prospecting-target-area was generated based on the object-oriented \(J\) map. These experiments imply that: \(D_{2}\), \(J\), as well as other manually designed features may not be a good indicator to separate multi-elemental anomalies from background, and alternatively, deep features produced by, e.g., OGE usually perform well in this case. Fourth, the delineated anomalous area can be dramatically reduced by increasing the binarization threshold. For example, for the S-series elements, when setting the binarization threshold as the 60% quantile (0.10562), the anomalous area decreases to 40.005% of the total area, but 6 ore-spots are excluded from the buffered anomalous regions; when setting the threshold as the 70% quantile (0.12625), the anomalous area decreases to 29.872%, but 14 ore-spots are excluded; when setting the threshold as the 80% quantile (0.16079), the anomalous area decreases to 20.157%, but 22 ore-spots are excluded; when setting the threshold as 0.2202 - the 90% quantile, the anomalous area decreases to 10.022%, but 34 ore-spots fall outside the buffered anomalous regions, and only 13 ore-spots fall within. The case is similar for the I-series elements. That is to say, the anomaly scores of the ore-containing image objects vary in a wide range, from the 50% quantile to 100%. In our opinion, this can be mainly attributed to the low resolution of the geochemical data in use and its weak ability in detecting the micro-mineralization outcrops. To prove this, Figure 16 further gives the anomalous regions extracted by 1:50,000 soil/stream-sediment survey data, which is generated using the geochemical dataset consisting of 12 ore-forming elements (Ag, As, Au, Bi, Cu, Hg, Mo, Pb, Sb, Sn, W, and Zn) as input to OGE. In Figure 16, we raise the binarization threshold from the 50% quantile (median) to the 60% quantile, and the anomalous area decreases to 38.11% of the total area accordingly. Comparing to Figure 11, a 11.77% reduction in anomalous area has been achieved without affecting the recognition rate: only 7 ore-spots fall outside the buffered anomalous area. If we raise the threshold to the 70% quantile, the anomalous area decreases to 29.54%, and only 11 ore-spots fall outside (the recognition rate remains above 80%). Obviously, due to the improved spatial resolution, our OGE model is guided to focus more on regional mineralization, rather than diagenesis. Fifth, in the pre-processing stage, first we interpolate sampled point data into raster grids, and then, they are aggregated into different image objects, which inevitably introduces uncertainties into the data. Naturally, people may question if a reduced segmentation scale can help improve the anomaly-detection performance. To answer this question, we use the scale parameter 2.0 to segment the input geochemical image, and finally we get 2465 objects as displayed in Figure 17. Taking the S-series elements as example, comparing to Figure 10 (upper), more details about the anomalies have emerged, but their spatial distribution patterns are generally consistent with those in Figure 10. We still have to set the binarization threshold as median to keep the ore-spot recognition rate greater than 90%, whereas if we raise the threshold to the 70% quantile, only 9 mineral spots fall outside the buffered anomalous area, and the recognition rate remains greater than 80%. Figure 18 gives the big picture: when we set the threshold smaller than or equal to the 60% quantile, the OGE analysis performed at higher levels of scale has higher recognition rate; whereas when we set the threshold greater than the 60% quantile, lower levels of scale may perform better than 10% quantile. Figure 16: The buffered anomalous regions (blue colored patches edged with white) with all the known ore spots overlaid on it. Note that the anomalous regions are generated by using the 1:50,000 geochemical dataset consisting of 12 ore-forming elements (Ag, As, Au, Bi, Cu, Hg, Mo, Pb, Sb, Sn, W, and Zn) as input to OGE.