## 3 Results
### The Object-Based Anomaly Score Map  
For the I-series elements, it can be seen from Figure 9 that: (1) most of the known ore spots/deposits, no matter what their scales and mineral species, fall within the image objects having higher anomaly scores (brighter colored areas). (2) Using the median value as the threshold, there are 30 ore spots falling within the delineated anomalous regions, and 13 spots falling outside. The recognition rate is 69.767%. (3) Using the 60 th percentile value as the threshold, there are 26 ore spots falling within the Level I anomalies, and the recognition rate is 60.465%. (3) The Level II anomalous area accounts for 49.778% of the total area, and the Level I area accounts for 40.079%. (4) Many of the anomalous objects are barren, especially those on the northwest corner of the study area. (5) Most of the mineral spots except Au falling outside the anomalous regions occur nearby the anomalous boundaries. If we create a 4-pixel buffer zone around the Level II anomalous objects, there are only 4 ore spots falling outside, and the ore-spot recognition rate comes up to 90.70%. (6) If we create a buffer zone around the Level I anomalies, the recognition rate increases to 74.419%. For the S-series elements, it can be seen from Figure 10 that: (1) there are 40 ore spots falling within the Level II anomalous regions, and only 7 spots falling outside. The recognition rate is 85.106%. (2) there are 33 ore spots falling within the Level I anomalies, and the recognition rate is 70.213% (3) most of the ore spots falling outside the anomalous regions occur nearby the anomalous boundaries, and relevant anomaly scores fluctuate between 0.030 and 0.070. If we create a 4-pixel buffer zone around the Level II anomalous patches, there are only one fluorite ore-spot falling outside, and the recognition rate comes up to 97.87%. (4) If we create a buffer zone around the Level I anomalies, the recognition rate increases to 78.723%. (5) The Level II anomalous area without the buffer zone accounts for 50.054% of the total area, and the buffered anomalous area accounts for 68.24%. (6) The spatial distribution of the anomalous regions in Figure 10 is generally consistent with that in Figure 9.  
Figure 8: Variation in loss and the recognition rate with number of training epochs. Note that: here we set the threshold of the anomaly score map as median, and the recognition rate is calculated as the ratio of the number of ore spots falling within the anomalous areas to the total number of known ore spots. Figure 9: **(upper)** the object-based anomaly-score base map with known I-series ore spots (red squares) overlaid on it. The image objects with larger scores are represented by brighter tones, while image objects with smaller scores are represented by darker tones. **(lower)** the delineated anomalous regions (green-colored patches—Level II anomalies, orange-colored patches—Level I anomalies, delineated by the 60% quantile) with ore spots overlaid on it. We also created a 4-pixel buffering zone (white colored belts) around the known anomalous regions. Note: the mineral species include Cu, Au, Fe, Mo, Hg, Ag-Pb-Zn-Fe, Ag-Pb-Zn, Pb-Zn, Cu-Zn, Ag-Pb, Pb, Zn, and U. In the lower picture, the ore spots not falling in the anomalous objects are labelled with their mineral species. The size of the ore spots is proportional to the mineralized scale ranging from mineralized spots to small- and medium-scaled ore deposits. Actually, most of the ore spots shown in Figure 9 are mineralized spots, namely micro-mineralization outcrops. The ore-spot data are provided by reference [29]. Figure 11 gives the anomaly score map of all the ore-forming elements, and we can observe that: (1) The Level II anomalous area accounts for 49.881% of the total area. (2) There are 50 ore spots falling within the Level II anomalous regions, and 18 ore spots falling within the Level II anomalous region. (3) The Level II anomalous region is the 1  
outside. (3) The recognition rate of the Level I anomalies is 66.176%.