*3.3. 3D Prospectivity Modeling Model and 3D Prediction Data Set Construction*

The 3DMPM method is mainly based on expert experience, the metallogenic model, and the exploration model summarized by the predecessors to obtain the MPM model. Various spatial analysis methods are used to analyze the deep 3D geological model and related metallogenic indicative features and obtain quantitative results. Based on this information, prediction information is constructed. Finally, the metallogenic favorable degree is calculated. The prospecting target area is delineated for the position with the high metallogenic favorable degree. Thus, it provides a new quantitative prediction support for ore prospecting on the deep edge of the mining area.

We took the skarn type deposit represented by Magushan Cu-Mo deposit in the study area as the research object. Firstly, according to the geological and metallogenic characteristics of the Magushan skarn Cu-Mo deposit [43], the metallogenic law and prospecting signs of the skarn copper deposit in the study area were summarized. A 3DMPM model was constructed. It includes prediction elements such as the Carboniferous stratigraphic contact surface, the Permian stratigraphic contact surface, the Triassic stratigraphic contact surface, the rock mass contact zone, and the diorite uplift position.

After that, combined with the 3DMPM model in the study area, the 3D geological model was further analyzed in 3D space. The 3D prediction elements were extracted. The 3D geological body surface extraction method is mainly used for the Triassic stratigraphic contact surface, and rock mass contact zone are extracted, respectively; the 3D geological structure surface analysis function extracts the uplift position of the diorite rock mass. The analysis and extraction methods are shown in Table 1:

**Table 1.** 3DMPM model and analysis and extraction method of ore control and indicator elements. (Modified from Ye [43]).


Based on the constructed 3D block model of the Xuancheng–Magushan area, this paper constructs the sample data period. The parameters of the 3D model are defined as shown in Table 2. The predicted depth is in the shallow space range of −3000 m. A single predicted cubic unit is defined as 100 m × 100 m × 25 m. The predicted space has 7.0735 million cubic units (Figure 6). *Minerals* **2022**, *12*, 1174 8 of 14

> **Table 2.** Definition of spatial parameters for 3DMPM in Xuancheng–Magushan Area. **Table 2.** Definition of spatial parameters for 3DMPM in Xuancheng–Magushan Area.


Based on the geological and metallogenic characteristics of the Magushan skarn Cu-

In order to fully explore the nonlinear relationship between the 3D ore-controlling factors and the ore-forming facts, based on the sample data set established above, this paper selects two machine learning methods, logical regression and random forest, to

In addition to the support of a large number of effective datasets, the machine learning model also needs to set the model's parameters for the current dataset, which is an important factor in determining the model's performance. The random forest algorithm includes the two most important parameters: the number of decision trees M and the num-

carry out 3D ore-forming predictions in the deep part of the mining area.

contact surface, the Triassic stratigraphic contact surface, the rock mass contact zone, and the diorite uplift position. A sample dataset for MPM was constructed by combining the metallogenic facts. In order to verify the generalization ability of the prediction model in the study area, a north–south division was made according to the known ore body locations. The south is used as a training area for the model to learn nonlinear ore-controlling characteristics, and the north is used as a test area to test the model's performance. There are 730 known ore body unit blocks in the study area, all of which are used as positive sample units, of which 614 were placed in the training set, and 116 were placed in the test set. To ensure a balance of positive and negative samples, 1500 non-ore body units around the known ore body were selected as negative samples. Of these, 1200 were put into the

**Figure 6.** Block model of Xuancheng–Magushan Area. **Figure 6.** Block model of Xuancheng–Magushan Area.

training set, and 300 were put into the test set.

**4. Prospectivity Modeling Process and Results**

*4.1. Predictive Model Building*

Based on the geological and metallogenic characteristics of the Magushan skarn Cu-Mo deposit, this study summarizes the metallogenic regularity and prospecting markers of the skarn copper deposit in the study area. Prediction factors include the stratigraphic contact surface, the Triassic stratigraphic contact surface, the rock mass contact zone, and the diorite uplift position. A sample dataset for MPM was constructed by combining the metallogenic facts. In order to verify the generalization ability of the prediction model in the study area, a north–south division was made according to the known ore body locations. The south is used as a training area for the model to learn nonlinear ore-controlling characteristics, and the north is used as a test area to test the model's performance. There are 730 known ore body unit blocks in the study area, all of which are used as positive sample units, of which 614 were placed in the training set, and 116 were placed in the test set. To ensure a balance of positive and negative samples, 1500 non-ore body units around the known ore body were selected as negative samples. Of these, 1200 were put into the training set, and 300 were put into the test set.

#### **4. Prospectivity Modeling Process and Results**
