**4. Prediction of Deep Prospecting Target Areas**

*4.1. Deep Prospecting Target Areas in the Jiaojia Fault*

#### 4.1.1. Determining Cell Blocks and Extracting Favorable Mineralization Information

The range and basic parameters of the modeling of the Jiaojia fault were determined based on the existing geological data of orebodies in the fault (especially the distribution of exploration lines), as well as the morphology, attitude, and spatial distribution of known orebodies. The established 3D geological model was divided into 660,800 cell blocks based on the line spacing × column spacing × layer spacing of 120 m × 120 m × 10 m. Among these cell blocks, 7263 were occupied by orebodies (also referred to as ore-hosting cell blocks). Ore-hosting cell blocks were assigned 1, while other cell blocks were assigned 0. Subsequently, the ore-controlling factors of ore-hosting cell blocks were extracted and their statistics were obtained, and accordingly, a quantitative predictive model was determined. Then, the predictive parameters were assigned to each cell block as per its attributes.

a. Cell blocks with favorable information of ore-hosting faults.

 <sup>1</sup> Structural buffer zones. Gold deposits are strictly controlled by faults, and gold orebodies mainly occur in the fractured alteration zones on the footwall of the main fault plane with fault gouges as the roof, with a small number of gold orebodies occurring on the hanging walls of the main fault plane. Therefore, the zone between 300 m above the footwall and 100 m below the hanging wall of the main fault plane was defined as a structural buffer zone favorable for mineralization. In this study, the structural buffer zones covered 108,572 cell blocks (Figure 3), which accounted for 16.43% of the total cell blocks in the model (660,800). Moreover, the structural buffer zones included 7124 ore-hosting cell blocks, accounting for 98.09% of the total ore-hosting cell blocks (7263) in the model. <sup>2</sup> The turning parts of the fault dip angles. Orebodies are mainly rich in stepped fault parts with gentle dip angles. In this study, the fault parts with a steep-to-gentle transition of fault dip angle covered 53,285 cell blocks, which accounted for 8.06% of the total cell blocks in the

model. These parts included 2866 ore-hosting cell blocks, which accounted for 39.46% of the total ore-hosting cell blocks in the model. <sup>3</sup> The changing rate of fault dip angle. The fault surfaces were divided into a number of square blocks, to each of which a slope attribute was assigned. The variance of the slope values of square blocks within a fixed range was calculated to determine the changes in the fault surface. The changing rate of fault dip angle involved 99,776 cell blocks, which accounted for 15.10% of the total cell blocks in the model. Furthermore, the changing rate of fault dip angle involved 5670 ore-hosting cell blocks, which accounted for 78.07% of the total ore-hosting cell blocks in the model; fixed range was calculated to determine the changes in the fault surface. The changing rate of fault dip angle involved 99,776 cell blocks, which accounted for 15.10% of the total cell blocks in the model. Furthermore, the changing rate of fault dip angle involved 5670 ore-hosting cell blocks, which accounted for 78.07% of the total ore-hosting cell blocks in the model; b. Cell blocks with favorable information of orebody distribution. By extracting the distribution range of ore bodies and mineralization enrichment

of fault dip angle covered 53,285 cell blocks, which accounted for 8.06% of the total cell blocks in the model. These parts included 2866 ore-hosting cell blocks, which accounted for 39.46% of the total ore-hosting cell blocks in the model. ③ The changing rate of fault dip angle. The fault surfaces were divided into a number of square blocks, to each of which a slope attribute was assigned. The variance of the slope values of square blocks within a

b. Cell blocks with favorable information of orebody distribution. areas, it can be seen that the ore body enrichment areas are nearly equally spaced along

*Minerals* **2022**, *12*, x FOR PEER REVIEW 8 of 22

By extracting the distribution range of ore bodies and mineralization enrichment areas, it can be seen that the ore body enrichment areas are nearly equally spaced along the strike and dip at about 500 m intervals (Figure 4). Accordingly, 273,240 cell blocks were divided along the strike with equal spacing, which accounted for 41.35% of the total cell blocks in the model. These cell blocks included 5932 ore-hosting cell blocks, which accounted for 81.67% of the total ore-hosting cell blocks. The orebody concentration areas showing equidistant distribution along fault dip directions included 251,856 cell blocks, which accounted for 38.11% of the total cell blocks in the model. These cell blocks included 5884 ore-hosting cell blocks, which accounted for 81.01% of the total ore-hosting cell blocks in the model. the strike and dip at about 500 m intervals (Figure 4). Accordingly, 273,240 cell blocks were divided along the strike with equal spacing, which accounted for 41.35% of the total cell blocks in the model. These cell blocks included 5932 ore-hosting cell blocks, which accounted for 81.67% of the total ore-hosting cell blocks. The orebody concentration areas showing equidistant distribution along fault dip directions included 251,856 cell blocks, which accounted for 38.11% of the total cell blocks in the model. These cell blocks included 5884 ore-hosting cell blocks, which accounted for 81.01% of the total ore-hosting cell blocks in the model.

**Figure 3.** Distribution of cell blocks covered by structural buffer zones in the Jiaojia fault. **Figure 3.** Distribution of cell blocks covered by structural buffer zones in the Jiaojia fault.

**Figure 4.** Horizontal projection of orebodies along fault strikes (**a**) and dip directions (**b**) in the Jiaojia deposit concentration area. The red dotted line in figure (**a**) and the green dotted line in figure (**b**) show the equally spaced distribution of orebody enrich areas. **Figure 4.** Horizontal projection of orebodies along fault strikes (**a**) and dip directions (**b**) in the Jiaojia deposit concentration area. The red dotted line in figure (**a**) and the green dotted line in figure (**b**) show the equally spaced distribution of orebody enrich areas.

#### 4.1.2. 3D Predictive Model and Statistics of Information Amounts 4.1.2. 3D Predictive Model and Statistics of Information Amounts

The deep prospecting predictive model of the Jiaojia fault (Table 1) was established based on the 3D geological model and the favorable metallogenic information extracted from the geological model. This predictive model involved eight characteristic variables for statistical analysis. They include Early Precambrian metamorphic rock series, Linglong granite, contact zone of Early Precambrian metamorphic rock series and Linglong granite, fault buffer zone, gentle part of a fault section with a steep-to-gentle transition of fault dip angle, changing rate of fault dip angle, equidistant distribution along fault strike and equidistant distribution along fault dip direction, respectively. Each characteristic variable was assigned 1 if it was true for a cell block and 0 otherwise. Based on the statistics of these characteristic variables of each cell block, the sum of the values of these characteristic variables of each cell block was calculated as the information amount for metallogenic prediction). The deep prospecting predictive model of the Jiaojia fault (Table 1) was established based on the 3D geological model and the favorable metallogenic information extracted from the geological model. This predictive model involved eight characteristic variables for statistical analysis. They include Early Precambrian metamorphic rock series, Linglong granite, contact zone of Early Precambrian metamorphic rock series and Linglong granite, fault buffer zone, gentle part of a fault section with a steep-to-gentle transition of fault dip angle, changing rate of fault dip angle, equidistant distribution along fault strike and equidistant distribution along fault dip direction, respectively. Each characteristic variable was assigned 1 if it was true for a cell block and 0 otherwise. Based on the statistics of these characteristic variables of each cell block, the sum of the values of these characteristic variables of each cell block was calculated as the information amount for metallogenic prediction).

#### 4.1.3. Predicted Results 4.1.3. Predicted Results

All the eight characteristic variables were assessed using the information amount method, obtaining metallogenic information amounts, which were then assigned to block models. Cell blocks with high information amounts have a high mineralization probability. Table 2 shows the statistics of the proportion of various information amount intervals of cell blocks. The information amount scopes where the information amounts tend to stabilize and converge are favorable for mineralization. According to the stability analysis of the proportion statistics, the information amounts tended to stabilize and converge when they were ≥3.222 (Figure 5). Therefore, the information amount interval of ≥3.222 was the favorable interval for mineralization (Figure 6a). This information amount interval was divided into three grades: 3.222–4.184, 4.184–5.018, and ≥5.018. The information amount range of ≥5.018 was regarded as the range of predicted target areas based on the comparison with known mineralized zones which are generally greater than 4.5 (Figure 7). As a result, two predicted target areas were delineated in the Jiaojia fault (Figure 6b). All the eight characteristic variables were assessed using the information amount method, obtaining metallogenic information amounts, which were then assigned to block models. Cell blocks with high information amounts have a high mineralization probability. Table 2 shows the statistics of the proportion of various information amount intervals of cell blocks. The information amount scopes where the information amounts tend to stabilize and converge are favorable for mineralization. According to the stability analysis of the proportion statistics, the information amounts tended to stabilize and converge when they were ≥3.222 (Figure 5). Therefore, the information amount interval of ≥3.222 was the favorable interval for mineralization (Figure 6a). This information amount interval was divided into three grades: 3.222–4.184, 4.184–5.018, and ≥5.018. The information amount range of ≥5.018 was regarded as the range of predicted target areas based on the comparison with known mineralized zones which are generally greater than 4.5 (Figure 7). As a result, two predicted target areas were delineated in the Jiaojia fault (Figure 6b).


**Table 1.** Deep predictive model of the Jiaojia fault.

**Table 2.** Proportion of metallogenic information amount intervals of cell blocks of the Jiaojia fault.


**Figure 5.** Histogram showing the proportion of metallogenic information amount intervals of cell blocks of the Jiaojia fault. **Figure 5.** Histogram showing the proportion of metallogenic information amount intervals of cell blocks of the Jiaojia fault. **Figure 5.** Histogram showing the proportion of metallogenic information amount intervals of cell blocks of the Jiaojia fault. **Figure 5.** Histogram showing the proportion of metallogenic information amount intervals of cell blocks of the Jiaojia fault.

**Figure 6.** Advantageous metallogenic target (**a**) and predicted target areas (**b**) of deep metallogenic in the Jiaojia fault.

**Figure 6.** Advantageous metallogenic target (**a**) and predicted target areas (**b**) of deep metallogenic in the Jiaojia fault. **Figure 6.** Advantageous metallogenic target (**a**) and predicted target areas (**b**) of deep metallogenic in the Jiaojia fault. **Figure 6.** Advantageous metallogenic target (**a**) and predicted target areas (**b**) of deep metallogenic in the Jiaojia fault.

**Figure 7.** Histogram of metallogenic information amount showing the mineralized zones and the barren zones in the Jiaojia fault. **Figure 7.** Histogram of metallogenic information amount showing the mineralized zones and the barren zones in the Jiaojia fault. **Figure 7.** Histogram of metallogenic information amount showing the mineralized zones and the barren zones in the Jiaojia fault.

Target area I (Beijueyujia) is located in Beijueyujia Village, Jincheng Town, Laizhou City. It has an elevation of −2349–−1154 m and included 1201 predicted ore-hosting cell blocks. Target area II (Xiji) lies in Xiji Village, Jincheng Town, Laizhou City. It has an elevation range of −2490–−2000 m and includes 1588 predicted ore-hosting cell blocks.
