# Prediction of Prospecting Target Based on ResNet Convolutional Neural Network
## 4 Results and Discussion
### Experiments Based on ResNet-50  
The experimental platform is Tesla V100 32 GB GPU, and the deep learning framework is Pytorch. Accuracy, AUC and F1 values are used to verify the performance of the model. In this study, the ResNet-50 convolutional neural network was used to train the comprehensive training set, and the SGD optimizer was used for training [44; 45]. The momentum is 0.1, the weight decay is 1e-4, the loop iteration is 120 times and the initial learning rate is 0.01, every 30 times. The attenuation is 50%, and the batch size is 32. Table 2 shows the experimental results of ResNet-50 compared with various popular CNN algorithms. ResNet is superior to other algorithms in Accuracy, AUC and Recall. This proves the effectiveness of using Resnet for prospecting target prediction. The training results of the experimental training set are shown in Figure 6. With the increase in training times, the loss of cross entropy decreases and the accuracy increases. Figure 6a shows the accuracy of training results. After the 60 th iteration, the increase rate of accuracy began to decline until the 95 th iteration began to stabilize, the accuracy rate was around 99% and the highest accuracy reached 99.31%. Figure 6b shows the cross-entropy loss image. The cross-entropy loss decreases after 40 iterations and becomes stable after 100 iterations. And the cross-entropy loss is about 0.04 and the lowest cross-entropy loss is 0.02381. The experimental results show that the model can predict the prospecting target area effectively after training. In this study, accuracy AUC and F1 scores were used as indicators to evaluate the prediction performance of the neural network model. F1 score is an indicator to measure the performance of the binary model. F1 values of each category were calculated and then averaged. The evaluation index of the prediction results of this experiment are with an accuracy of 86.11%, AUC of 0.7454 and F1 score of 0.7653, respectively. The experiment shows that it is effective and reliable to use the existing geochemical data to train the neural network model and use the model to predict the ore target area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **AUC** & **Recall** \\ \hline MobileNet [46] & 55.82 & 55.44 & 16.66 \\ \hline GoogleNet [47] & 54.36 & 54.03 & 19.60 \\ ShuffleNet [48] & 55.33 & 54.97 & 17.64 \\ \hline ResNet & 56.79 & 56.53 & 24.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results. Figure 6: The accuracy and entropy loss of experiments. Figure 7 is a visual display of prediction results. For 144 data in the test set, numbered from 1 to 144, a total of 23 regions are predicted to be favorable metallogenic target areas, and 121 regions are general areas. In 23, an advantage, including mining domain, numbers 27, 29, 50, 56, 62, 64, 66, 85, 96, 97, 107, 113, 141, 142, 143 consistent with research in the known ore target area, is given to illustrate the effectiveness of the model prediction results. While the eight regions, including 9, 38, 40, 95, 111, 114, 124, 144, are the metallogenic prospects predicted by this research model, and are not marked as ore area data at present, so it is predicted that these eight regions have great ore-bearing potential. Figure 8 is a confusion matrix, showing the number of True Negative (TN), False Positive (FP), False Negative (FN) and True Positive (TP) samples. From the figure, we can see that TN is the highest, followed by TP. This proves that ResNet-50 can effectively identify samples without mines and has a certain effect on the prospecting target prediction. Figure 7: The prediction results.