# Prediction of Prospecting Target Based on ResNet Convolutional Neural Network
## 1 Introduction  
Geological and mineral exploration is the core work of geologists to provide resources and an energy guarantee for the stable development of society, and its main task is to use scientific and technological methods to carry out reasonable metallogenic prediction, and to discover and increase the reserves of resources [1]. In recent years, with the decrease in surface ore and shallow ore, the difficulty of prospecting is increasing. Geological prospecting and exploration work is gradually developing towards deep ore and hidden ore. At present, the difficulties of geological and mineral exploration are mainly reflected in the complex geological overlay, deep deposit burial resulting in weak prospecting information,complex deposit preservation environment factors, multi-stage mineralization resulting in complex metallogenic information and so on. In order to solve the above problems, geological experts and scholars pay more and more attention to the application of computer technology and new methods in geological and mineral exploration research [2; 3; 4; 5; 6; 7]. In recent years, with the rapid development and gradual maturity of computer technology and artificial intelligence technology, it has become possible to introduce computer technology and deep learning methods into the field of geological prospecting and exploration. The mining and prediction of geological prospecting information based on deep learning have become a hot research field in earth science. As a deep artificial intelligence algorithm, deep learning has been actively applied in the field of earth science research and has made many innovative achievements [8; 9; 10; 11; 12; 13]. The authors of [14] summarized the achievements of artificial intelligence and big data technology in the field of earth science in the past, and proposed that artificial intelligence and big data technology would be the main development direction of earth science research in the future. The authors of [15] used deep learning technology to predict the baseline map of geological disasters in Australia, and the algorithm model obtained can provide technical support for related fields. In [16], the authors established the HPC cluster platform, which provided platform support for the application of deep learning technology in geosciences scientific data. The authors of [17] used the deep learning algorithm and big data analysis to draw the mineral prospect map, and the prediction results showed that the drawn prospect area had a strong spatial relationship with the known mineralization location. The authors of [18] used machine learning models and geostatistical methods to predict the geochemical concentration of iron in the iron ore research area in southern Cameroon. The results showed that random forest was the algorithm of choice for predicting Fe geochemical concentrations. The authors of [19] compared the application of multiple deep learning methods in intelligent mineral identification of different data types, and proposed that transfer learning can effectively solve the problems of insufficient geological data samples and class imbalance. The authors of [20] used deep learning methods to perform intelligent recognition of polarizing microscopic images of five minerals and obtained good recognition results. The authors of [21] used principal component analysis, cluster analysis and partial least squares dimensionality reduction method to conduct targeted prospecting prediction for stream sediment geochemical survey data. The analysis results have a high degree of coupling with known geological conditions of ore deposits, which can provide reliable data support for further prospecting in this area. In [22], the authors used the random forest algorithm to identify geochemical anomalies and delineate three-dimensional metallogenic prospects. The authors of [23] used the convolutional neural network to predict lead-zinc deposits in Anhui Province and successfully delineated three prospecting targets.