Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example  

作　　者：Yong-sheng Li Chong Peng Xiang-jin Ran Lin-Fu Xue She-li Chai 

机构地区：[1]Development and Research Center of China Geological Survey,Ministry of Natural Resources,Beijing 100037,China [2]College of Earth Sciences,Jilin University,Changchun 130061,China [3]Technical Guidance Center for Mineral Exploration,Ministry of Natural Resources,Beijing 100083,China [4]College of Geographical Sciences,Shanxi Normal University,Linfen 041004,China [5]College of Geo-Exploration Science and Technology,Jilin University,Changchun 130026,China

出　　处：《China Geology》2022年第1期71-83,共13页中国地质（英文）

基　　金：funded by a pilot project entitled“Deep Geological Survey of Benxi-Linjiang Area”(1212011220247)of the 3D Geological Mapping and Deep Geological Survey of China Geological Survey。

摘　　要：A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

关 键 词：Soil geochemistry Spatial feature matching Gold deposit Deep learning Mineral prospecting prediction model Data augmentation mineral exploration engineering Gansu Province China 

分 类 号：P618.51[天文地球—矿床学] P632.1[天文地球—地质学]