# Prediction of Prospecting Target Based on ResNet Convolutional Neural Network
## 5 Conclusions  
Based on artificial intelligence deep learning, this paper uses the convolutional neural network to learn the spatial distribution relationship between stream sediment geochemical element datasets and known minerals in the Pangxidong research area, Guangdong Province. A variety of machine learning methods were used to construct and test the algorithm model, and metallogenic prediction and delineation of prospecting targets were carried out to explore the possibility of geological and mineral mineralization prediction based on the deep learning algorithm. The main conclusions are as follows:  
(1) When the number of training samples of geological and mineral images is small, deep learning is also feasible for metallogenic prediction, which can achieve the goal of automatic data acquisition and metallogenic prediction by computer. In this study, through the ResNet-50 convolutional neural network, the goal of researchers' input of geochemical and geological data and computer intelligent prediction has been preliminarily achieved.  
(2) The support vector machine method was used to process 16 kinds of geochemical data to obtain the AUC value of each element, and then the ZAUC value was calculated by statistical method. The eight elements, Au, Sn, Cu, Ag, Ba, Sb, Hg and Mo, were identified as ore-related elements by comparison with the standard positive gravity distribution table. The influence of unrelated geochemical elements on the experiment is greatly reduced, and the complexity of the neural network model is also reduced.  
(3) The convolutional neural network can effectively explore the coupling relationship between surface geochemical element concentration distribution and underground mineral space emplacement. After 95 training times in this study, the accuracy of the ResNet-50 convolutional neural network model is 99%, and the loss entropy is 0.04. We can consider that the neural network model is successfully trained and the accuracy is effective and reliable. The neural network model is scientific and effective in predicting the ore-forming target area in the experimental area.  
(4) The study area was divided into 144 subregions (12 \(\times\) 12), and the two trained models were used for prediction, and the experimental results confirmed each other. The accuracy of the experimental results was 86.11%. Among them, 15 ore-bearing regions coincide with the known ore-bearing regions. In addition, the model of this study predicts that these areas, numbered 9, 38, 40, 95, 111, 114, 124, 144, have a high probability of mineralization, which can be further studied and exploration recommendations can be made for these areas.  
(5) In order to verify the generality of the research method in this paper, we conducted experimental tests on the geochemical dataset of study area B of the deposit. The experimental results show that 100% of the prediction area obtained by the research method in this paper covers the known ore deposit area, and the model provides a scientific basis for the prediction of metal ore in study area B, indicating that the model has universal applicability.  
L.G.: Formal analysis, Investigation, Funding management. Y.H.: Model building, Coding, Writing--original draft. Q.L.: Writing--original draft, Formal analysis. X.Z.: Data collection. Z.C.: Text proofreading, Model analysis. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Wuyi University Youth Research Group Fund grant number (2019 td10), Wuyi University Hongkong Macao Joint Research and Development Fund grant number (2019 WGALH23) and National Natural Science Foundation of China grant number (U1911202).  
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.